SARS COVID 19 Classification

A Dissertation Submitted in Partial Fulfillment for the Requirement of the Degree of

MCA ( Master of Computer Applications)
Batch 2022 - 24

Guided By Submitted By

Prof. Anel Gupta Gaurav Solanki

Maharaja Ranjit Singh Group of Institutions

[MCA Institute] An ISO 2009 Certified Institute
Hemkunt Campus, Khandwa Road, Indore
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya
 SARS COVID 19 Classification

A Dissertation Submitted in Partial Fulfillment for the Requirement of the Degree of

MCA ( Master of Computer Applications)
Batch 2022 - 24

Guided By Submitted By

Prof. Anel Gupta Gaurav Solanki

Maharaja Ranjit Singh Group of Institutions

[MCA Institute] An ISO 2009 Certified Institute
Hemkunt Campus, Khandwa Road, Indore
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya
 Approval Sheet

The project titled “SARS COVID 19 Claassification“ for “Maharaja Ranjit Singh Group of
Institute” has been submitted by Mr. Gaurav Solanki and is approved in partial fulfillment for
the requirement of degree of MCA (Master of Computer Applications).

Internal Examiner External Examiner

Director
 Authentication Certificate

The project titled “SARS COVID 19 Classification” has been

accomplished by me. It is an original work and is not submitted
to any other College/University.

I submit this report in partial fulfillment for the requirement of

degree of MCA (Master of Computer Applications).

Gaurav Solanki

___________________
 Acknowledgement

We take this opportunity to express our deepest and sincere

gratitude to our guide Mr. Anil Gupta Sir, HOD of MCA
Department, Maharaja Ranjit Singh Group of Institution,
Indore for his insightful advice, motivating suggestions,
invaluable guidance, help and support in successful completion of
this project and also for her constant encouragement and advice.

We express our hearty gratitude to Mr. Parag Pandey Sir,

Professor of MCA Department, Maharaja Ranjit Singh Group
of Institution, Indore for her regular support, co-operation, and
co-ordination.

We are also grateful to our respected Director Dr. B.K.

Shrivashtava, REC Bhopal for permitting us to utilize all the
necessary facilities of the institution.

We are also thankful to all other faculty & staff member of our
department for their kind co-operation and help.Lastly we would
like to express our deep appreciation towards our classmates and
our parents for providing us the moral support and
encouragement.

We would like to convey our thanks to the teaching and non-

teaching staff of the Department of Electronics & Communication
and Computer Engineering, acme for their invaluable help and
support throughout the period of Master’s Degree.
Gaurav Solanki (0803CA221034)
 Abstract
The COVID-19 pandemic has highlighted the critical need for rapid and accurate diagnostic
tools. Computed Tomography (CT) scans have emerged as a valuable tool in diagnosing
COVID-19, offering detailed insights into the condition of a patient's lungs. This project aims to
develop a deep learning-based Convolutional Neural Network (CNN) model for the automatic
classification of CT scan images into COVID-19 positive or negative categories. Leveraging the
powerful feature extraction capabilities of CNNs, the proposed model aims to assist healthcare
professionals by providing a reliable, automated diagnostic tool that can complement existing
diagnostic methods and potentially expedite the treatment process.
The project encompasses several key stages: data collection, preprocessing, model design, training,
evaluation, and deployment. A comprehensive dataset of labeled CT scan images is utilized, incorporating
both COVID-19 positive and negative cases. Advanced image preprocessing techniques are applied to
enhance the quality and relevance of the input data. The CNN model is designed and trained to capture
intricate patterns and anomalies indicative of COVID-19 infection.
Extensive evaluation of the model is conducted using various performance metrics, including accuracy,
sensitivity, specificity, and AUC-ROC, to ensure its robustness and reliability. The resulting model
demonstrates promising performance, showcasing the potential of deep learning in medical image analysis
and its application in supporting the fight against COVID-19.
This project not only contributes to the body of research in medical imaging and deep learning but also aims
to provide a practical tool that can aid in the timely and accurate diagnosis of COVID-19, ultimately
enhancing patient outcomes and supporting public health efforts.
 Preface

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems,
necessitating the rapid development and deployment of innovative diagnostic tools. Among
the various imaging modalities, Computed Tomography (CT) scans have proven to be
invaluable in identifying the presence and severity of COVID-19 related lung abnormalities.
However, the manual interpretation of CT images is both time-consuming and subject to
variability among radiologists. This project was conceived to explore the application of deep
learning, specifically Convolutional Neural Networks (CNNs), to automate and enhance the
accuracy of COVID-19 diagnosis from CT scan images.
In undertaking this project, I have aimed to bridge the gap between cutting-edge artificial intelligence
technology and practical medical diagnostics. The primary objective is to develop a robust CNN model
capable of distinguishing between COVID-19 positive and negative cases with high accuracy, thereby
supporting healthcare professionals in making timely and informed decisions. This work is particularly
inspired by the potential of AI to transform medical imaging, reduce diagnostic time, and ultimately save
lives.
This preface serves to acknowledge the interdisciplinary nature of this endeavor, combining insights from
medical imaging, computer science, and deep learning. The project is a culmination of extensive research,
experimentation, and collaboration. I would like to express my gratitude to the following individuals and
organizations:
• Healthcare Professionals and Radiologists: For providing invaluable insights into the clinical
aspects of COVID-19 diagnosis and the significance of CT imaging.
• Academic Advisors and Mentors: For their guidance and support in navigating the complexities of
deep learning and medical image analysis.
• Data Providers and Researchers: For making available the comprehensive datasets that form the
backbone of this project.
• Technical Community: For the open-source tools and frameworks that have facilitated the
development of the CNN model.

In closing, I am reminded of the profound impact that interdisciplinary collaboration can have in addressing
global challenges. It is my belief that the synergy between technology and healthcare holds immense promise
for the future, and I am honored to contribute to this evolving field.
 Table of Contents

S.
Details Page No.
No.
Introduction
1. 1.1 Purpose. 10-20
1.2 Problem Statement
1.3 Project Background
1.4 Objective
1.5 Scope
1.6 Expected Benefits
1.7 Requirement and Constraints
1.7.1 Functional Requirements
1.7.2 Non-Functional Requirements
1.8 Platform
1.9 Methodology
1.10 Overview

2. Project Planing 21-23

3. Software Requirement Specification 24-26

4. Conceptual & Physical Design 27-28

5. System Testing 29-30

6. Summary of Experience gained 31-32

7. Conclusion 33-34

8. Scope of Development 35-36

9. References 36
10. I/O Screen 37
 1. Introduction

1.1 Purpose :

The primary purpose of this project is to develop a robust and accurate deep learning-based Convolutional Neural
Network (CNN) model for the automatic classification of CT scan images into COVID-19 positive and negative
categories. The motivation behind this endeavor stems from the urgent need for reliable diagnostic tools in the
face of the COVID-19 pandemic, which has overwhelmed healthcare systems worldwide.
Key objectives of this project include:
1. Enhancing Diagnostic Accuracy: By leveraging the powerful feature extraction capabilities of
CNNs, the project aims to achieve high diagnostic accuracy, reducing the risk of false positives and
negatives in COVID-19 detection. This can significantly improve patient outcomes by ensuring
timely and appropriate medical intervention.
2. Automating Image Analysis: Manual interpretation of CT scans is labor-intensive and subject to
human error. An automated CNN model can provide consistent and rapid analysis, thereby freeing up
valuable time for radiologists and healthcare professionals to focus on patient care.
3. Supporting Healthcare Professionals: The model is designed to function as an assistive tool for
radiologists, providing a second opinion and reducing the cognitive load during peak times of the
pandemic. This support is crucial in maintaining the quality of care amidst increased demand for
diagnostic services.
4. Contributing to Public Health Efforts: Early and accurate detection of COVID-19 is essential for
controlling the spread of the virus. By providing a scalable and efficient diagnostic solution, this
project contributes to broader public health efforts aimed at mitigating the impact of the pandemic.
5. Advancing Medical AI Research: This project also seeks to contribute to the body of knowledge in
the fields of medical imaging and artificial intelligence. The methodologies, findings, and insights
gained can serve as a foundation for future research and innovation in AI-driven healthcare solutions.
6. Facilitating Data-Driven Decisions: By integrating deep learning models into the diagnostic
workflow, healthcare institutions can harness the power of data-driven decisions, improving the
overall efficiency and effectiveness of medical diagnostics.
The successful implementation of this project has the potential to transform the diagnostic process for
COVID-19, offering a practical solution that enhances the capabilities of existing medical infrastructure. It
underscores the transformative potential of artificial intelligence in addressing global health crises and paves
the way for further advancements in AI-powered healthcare.

1.2 Problem Statement :

The COVID-19 pandemic has presented an unprecedented global health crisis, placing immense pressure
on healthcare systems and professionals. Rapid and accurate diagnosis of COVID-19 is critical for
 effective patient management and controlling the spread of the virus. Computed Tomography (CT)
scans have emerged as a valuable diagnostic tool, providing detailed images that can reveal lung
abnormalities associated with COVID-19. However, the manual interpretation of CT scans is time-
consuming, resource-intensive, and prone to variability among radiologists.
Despite their diagnostic value, the reliance on human interpretation of CT scans introduces several
challenges:
1. Time Consumption: Analyzing CT scans manually is a slow process, especially during peak times of
the pandemic when the volume of scans increases dramatically.
2. Subjectivity and Variability: The accuracy of diagnosis can vary based on the experience and
expertise of radiologists, leading to inconsistencies in identifying COVID-19 cases.
3. Resource Intensive: High demand for radiological assessments strains healthcare resources,
potentially delaying diagnosis and treatment.
Given these challenges, there is an urgent need for an automated, reliable, and efficient diagnostic tool that
can assist healthcare professionals by providing rapid and accurate analysis of CT scan images.
The primary problem this project addresses is:
How can we develop a deep learning-based Convolutional Neural Network (CNN) model to automate
the classification of CT scan images into COVID-19 positive and negative categories, thereby
enhancing diagnostic accuracy, reducing interpretation time, and supporting healthcare professionals
in managing the COVID-19 crisis more effectively?
The specific goals to solve this problem include:
• Data Acquisition and Preprocessing: Collecting a comprehensive dataset of labeled CT scan images
and applying preprocessing techniques to enhance the quality and relevance of the input data.
• Model Development: Designing and training a CNN model capable of accurately distinguishing
between COVID-19 positive and negative cases.
• Performance Evaluation: Assessing the model's performance using metrics such as accuracy,
sensitivity, specificity, and AUC-ROC to ensure its robustness and reliability.Deployment:
Developing a user-friendly interface and integrating the model into the diagnostic workflow to
facilitate its adoption in clinical settings.
By addressing these objectives, the project aims to provide a practical solution that not only improves
diagnostic efficiency but also supports the overall efforts to combat the COVID-19 pandemi
 1.3 Project Background :

The COVID-19 pandemic has overwhelmed healthcare systems globally, creating an urgent need for rapid
and accurate diagnostic tools. Computed Tomography (CT) scans have become a crucial method for
diagnosing COVID-19 due to their ability to reveal lung abnormalities associated with the virus. However,
manually interpreting these scans is time-consuming, resource-intensive, and subject to variability among
radiologists.

Artificial Intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNNs), have
shown significant potential in automating medical image analysis. These technologies can enhance diagnostic
accuracy, reduce interpretation time, and provide consistent results, making them ideal for addressing the
current challenges in COVID-19 diagnosis.
This project aims to develop a CNN model to automatically classify CT scan images as COVID-19 positive
or negative. By leveraging deep learning, the project seeks to support healthcare professionals, improve
diagnostic efficiency, and contribute to better patient outcomes during the pandemic.

1.4 Objective :

The primary objective of this project is to develop an accurate and efficient deep learning-based
Convolutional Neural Network (CNN) model for the automatic classification of CT scan images into
COVID-19 positive and negative categories. This objective encompasses several key goals:
1. Data Collection and Preprocessing: Assemble a comprehensive dataset of labeled CT scan images,
applying preprocessing techniques to enhance image quality and relevance for model training.
2. Model Design and Development: Design a CNN architecture tailored to the task of COVID-19
classification, optimizing the model to capture the relevant features and patterns indicative of the
disease.
3. Training and Evaluation: Train the CNN model on the preprocessed dataset and rigorously evaluate
its performance using metrics such as accuracy, sensitivity, specificity, and AUC-ROC to ensure its
robustness and reliability.
4. Performance Optimization: Implement techniques to fine-tune and optimize the model, enhancing
its ability to generalize across different datasets and reducing the risk of overfitting.
5. Deployment and Integration: Develop a user-friendly interface and integrate the model into the
clinical workflow, facilitating its adoption by healthcare professionals to assist in the rapid and
accurate diagnosis of COVID-19 from CT scan images.
By achieving these objectives, the project aims to provide a valuable tool that supports healthcare systems in
managing the COVID-19 pandemic, improves diagnostic efficiency, and contributes to better patient
outcomes.
 1.5 Scope :

The scope of this project encompasses the following key areas:

1. Data Acquisition: Collect a diverse and comprehensive dataset of CT scan images labeled as
COVID-19 positive and negative. This includes sourcing images from publicly available medical
databases and collaborating with healthcare institutions for additional data.
2. Data Preprocessing: Implement preprocessing techniques to prepare the CT scan images for training.
This includes normalization, resizing, augmentation, and handling imbalanced datasets to ensure high-
quality input for the model.
3. Model Design and Architecture: Develop a Convolutional Neural Network (CNN) model tailored
for the classification of CT scan images. The model architecture will be designed to capture intricate
patterns and features specific to COVID-19.
4. Model Training: Train the CNN model using the preprocessed dataset. This involves selecting
appropriate training parameters, optimizing the model using various techniques, and ensuring that the
training process is robust and effective.
5. Evaluation and Validation: Evaluate the model's performance using a variety of metrics, including
accuracy, sensitivity, specificity, and AUC-ROC. Validation will be performed using cross-validation
techniques and a separate validation dataset to ensure generalizability.
6. Performance Optimization: Fine-tune the model to improve its accuracy and generalization
capabilities. This includes techniques such as hyperparameter tuning, regularization, and using
advanced optimizers.
7. User Interface and Deployment: Develop a user-friendly interface for the model, making it
accessible for healthcare professionals. The model will be integrated into the clinical workflow to
support the rapid and accurate diagnosis of COVID-19 from CT scans.
8. Documentation and Reporting: Provide comprehensive documentation of the project, including
methodologies, model architecture, training processes, evaluation results, and user guidelines. This
ensures transparency and facilitates future research and development.
9. Ethical Considerations and Compliance: Ensure that all aspects of the project comply with ethical
standards and regulations concerning patient data privacy and security. This includes obtaining
necessary permissions and anonymizing patient data.
10. Future Work and Improvements: Identify potential areas for future improvements and
extensions of the project. This includes exploring additional data sources, enhancing model accuracy,
and adapting the model for other related diagnostic tasks.
The scope of this project aims to deliver a reliable, efficient, and scalable solution for the automatic
classification of CT scan images to aid in the diagnosis of COVID-19, ultimately supporting healthcare
professionals and improving patient outcomes.
 1.6 Expected Benefits :

The successful completion of this project is anticipated to deliver several significant benefits across various
domains, including healthcare, technology, and public health. The primary expected benefits are outlined
below:
1. Enhanced Diagnostic Accuracy:
• By leveraging the advanced pattern recognition capabilities of Convolutional Neural Networks
(CNNs), the project aims to achieve high diagnostic accuracy, reducing false positives and
negatives in COVID-19 detection.
• Improved accuracy can lead to better patient management and treatment outcomes, as early
and precise diagnosis is crucial for effective intervention.
2. Increased Diagnostic Efficiency:
• The automated classification of CT scan images will significantly reduce the time required for
diagnosis, enabling faster decision-making and treatment initiation.
• This efficiency is particularly beneficial during peak periods of the pandemic when the
demand for diagnostic services is exceptionally high.
3. Support for Healthcare Professionals:
• The CNN model can act as an assistive tool for radiologists and healthcare providers, offering
a second opinion and reducing the cognitive load associated with manual image interpretation.
• By providing consistent and reliable results, the model helps mitigate variability in diagnoses
due to differences in individual expertise.
4. Scalability and Accessibility:
• An automated diagnostic tool can be easily scaled to handle large volumes of CT scans,
making it accessible to healthcare facilities with varying resources and capabilities.
• This scalability is essential for addressing the diagnostic needs in both urban and rural settings,
ensuring equitable access to advanced diagnostic technology.
5. Public Health Impact:
• Rapid and accurate diagnosis of COVID-19 contributes to better disease management and
control, helping to curb the spread of the virus.
• The project supports public health efforts by facilitating early detection and isolation of
infected individuals, thereby reducing transmission rates.
6. Advancement of AI in Healthcare:
• The project contributes to the growing body of research on the application of artificial
intelligence in medical imaging, demonstrating the practical utility and impact of AI
technologies in real-world healthcare scenarios.
• It encourages further exploration and innovation in AI-driven diagnostic tools for various
medical conditions beyond COVID-19.
7. Resource Optimization:
 • By automating the diagnostic process, the project helps optimize the use of limited healthcare
resources, allowing radiologists and other healthcare professionals to focus on more complex
and critical cases.
• This optimization can lead to overall improvements in healthcare service delivery and patient
care.
8. Educational and Research Value:
• The methodologies, data, and findings from this project provide valuable educational
resources for students, researchers, and practitioners in the fields of AI and medical imaging.
• The project sets a foundation for future research, encouraging continued innovation and
development in AI-based diagnostic tools.
Overall, the expected benefits of this project highlight its potential to make a meaningful impact on the
diagnosis and management of COVID-19, supporting healthcare professionals and enhancing public health
outcomes through the application of cutting-edge AI technology.

1.7 Requirements And Constraints :

1.7.1 Functional Requirements:

1. Data Requirements:
• Labeled CT Scan Images: A comprehensive dataset of CT scan images labeled as COVID-19
positive and negative.
• Diverse Data Sources: Data should be sourced from multiple hospitals and regions to ensure
diversity and generalizability of the model.
2. Technical Requirements:
• Hardware: Access to high-performance computing resources, including GPUs, for training
deep learning models.
• Software: Use of deep learning frameworks such as TensorFlow or PyTorch for model
development and training.
• Preprocessing Tools: Software tools for image preprocessing, such as OpenCV and Scikit-
image.
3. Model Requirements:
• CNN Architecture: Design of an appropriate Convolutional Neural Network (CNN)
architecture tailored for the classification task.
• Training Algorithm: Implementation of efficient training algorithms with hyperparameter
tuning to optimize model performance.
• Performance Metrics: Use of metrics such as accuracy, sensitivity, specificity, and AUC-
ROC for evaluating model performance.
4. User Interface Requirements:
 • Intuitive Design: Development of a user-friendly interface for healthcare professionals to
interact with the model.
• Visualization Tools: Tools for visualizing CT scan images and the model’s classification
results.
5. Documentation and Reporting:
• Comprehensive Documentation: Detailed documentation of methodologies, model
architecture, training processes, and user guidelines.
• Result Reporting: Clear presentation of evaluation results, including performance metrics and
visualizations.
6. Compliance and Ethical Requirements:
• Data Privacy: Ensuring patient data is anonymized and handled in compliance with relevant
data protection regulations.
• Ethical Standards: Adherence to ethical standards in AI research and application, including
transparency and fairness.
Constraints
1. Data Constraints:
• Availability: Limited availability of high-quality, labeled CT scan datasets, particularly for
COVID-19 negative cases.
• Quality: Variability in image quality and resolution across different sources may impact model
training and performance.
2. Technical Constraints:
• Computational Resources: High computational demands for training deep learning models
may limit the ability to iterate and experiment with different architectures.
• Software Compatibility: Ensuring compatibility between different software tools and
frameworks used in the project.
3. Model Constraints:
• Overfitting: Risk of overfitting due to limited data or imbalanced datasets, necessitating
careful validation and regularization techniques.
• Generalization: Ensuring the model generalizes well across diverse datasets and is not overly
tuned to specific data characteristics.
4. Deployment Constraints:
• Integration: Challenges in integrating the model with existing clinical workflows and
healthcare IT systems.
• Usability: Ensuring the interface is user-friendly and does not require extensive training for
healthcare professionals.
5. Regulatory Constraints:
• Compliance: Adherence to healthcare regulations and standards, including those related to
medical devices and AI in healthcare.
 • Approval: Obtaining necessary approvals and certifications for deploying AI-based diagnostic
tools in clinical settings.

Ethical Constraints:
• Bias and Fairness: Mitigating potential biases in the model to ensure fair and equitable
diagnosis across different patient demographics.
• Transparency: Maintaining transparency in the model’s decision-making process to build
trust among healthcare providers and patients.

1.8 Platform :
Frontend
1. HTML, CSS, and JavaScript:
• The frontend of the platform is built using HTML for structure, CSS for styling, and
JavaScript for interactive elements.
• It provides a simple and intuitive user interface (UI) for healthcare professionals to interact
with the CNN model.
2. User Interface (UI):
• The UI displays a form where users can upload CT scan images for analysis.
• It includes interactive elements such as buttons for uploading images and submitting them for
classification.
3. Visualization Tools:
• The platform includes tools to visualize CT scan images and display the model's classification
results.
• Visualizations such as heatmaps or overlaying of identified features on the original image may
be included to aid in interpretation.
Backend
1. Python and Flask:
• Python is used for implementing the CNN model and Flask is used as a lightweight web
framework for the backend.
• Flask handles HTTP requests from the frontend, processes the uploaded CT scan images, and
returns the classification results to the user interface.
2. Convolutional Neural Network (CNN) Model:
• The CNN model is developed using deep learning frameworks such as TensorFlow or
PyTorch.
• It is trained on a dataset of labeled CT scan images to classify them as COVID-19 positive or
negative.
3. Data Handling and Preprocessing:
 • The backend includes modules for handling data preprocessing tasks, such as image
normalization, resizing, and data augmentation.
• These tasks ensure that the input images are in the correct format and quality for model
inference.
4. Model Inference:
• The Flask backend manages the inference process, where uploaded CT scan images are passed
through the trained CNN model to predict their classification.
• The results are computed and returned to the frontend in real-time, providing immediate
feedback to the user.

1.9 Methedology :
1. Problem Understanding and Definition
• Objective Definition: Clearly define the goal of the project, which is to develop a deep learning-
based CNN model for the automatic classification of CT scan images into COVID-19 positive and
negative categories.
• Scope Definition: Define the scope, requirements, and constraints of the project to ensure alignment
with stakeholders' expectations and project feasibility.
2. Data Collection and Preparation
• Dataset Acquisition: Gather a diverse and comprehensive dataset of CT scan images labeled as
COVID-19 positive and negative. Utilize publicly available datasets and collaborate with healthcare
institutions to collect additional data.
• Data Preprocessing:
• Image Preprocessing: Preprocess the CT scan images to standardize their format, size, and
resolution. This includes normalization, resizing, and data augmentation techniques to enhance
model training.
• Data Augmentation: Augment the dataset to increase its size and diversity, which helps in
improving the model's generalization ability.
• Dataset Splitting: Split the dataset into training, validation, and testing sets to evaluate the model's
performance objectively.
3. Model Development
• CNN Architecture Design: Design a Convolutional Neural Network (CNN) architecture suitable for
the classification task. Experiment with different architectures, such as ResNet, VGG, or custom
architectures, to identify the most suitable one.
• Model Implementation: Implement the chosen CNN model using deep learning frameworks such as
TensorFlow or PyTorch. Configure the model with appropriate layers, activation functions, and
optimizer algorithms.
• Hyperparameter Tuning: Optimize hyperparameters (e.g., learning rate, batch size) to improve the
model's performance and convergence during training.
 • Model Training: Train the CNN model on the preprocessed dataset using GPUs to accelerate the
training process. Monitor training metrics and adjust hyperparameters as needed to achieve optimal
results.
4. Model Evaluation and Validation
• Performance Metrics: Evaluate the model's performance using metrics such as accuracy, precision,
recall, F1-score, and area under the ROC curve (AUC-ROC).
• Validation Techniques: Perform cross-validation to validate the model's robustness and
generalizability across different datasets.
• Visualization: Visualize evaluation metrics, confusion matrices, ROC curves, and classification
heatmaps to interpret the model's performance and identify areas for improvement.
5. Deployment and Integration
• Web Application Development: Develop a web application using HTML, CSS, JavaScript, and
Flask.
• Backend Integration: Integrate the trained CNN model into the web application backend using Flask
to handle model inference requests.
• User Interface Design: Design a user-friendly interface for healthcare professionals to upload CT
scan images, visualize results, and interpret the model's predictions.
• Deployment: Deploy the web application on a cloud platform (e.g., AWS, GCP) using Docker
containers to ensure scalability and availability.
6. Documentation and Reporting
• Project Documentation: Document the entire methodology, including data collection, model
development, training, evaluation, and deployment processes.
• User Guide: Provide a user guide with instructions on how to use the web application, interpret
results, and troubleshoot common issues.
• Technical Report: Compile a comprehensive technical report detailing methodologies, experimental
results, performance metrics, and conclusions.
• Ethical Considerations: Address ethical considerations related to patient data privacy, bias
mitigation, and model transparency.
7. Iterative Improvement
• Model Iteration: Iterate on the model based on feedback, performance evaluations, and new data to
continuously improve its accuracy and robustness.
• Feature Enhancement: Incorporate advanced features such as explainability tools (e.g., Grad-CAM)
or ensemble learning techniques to enhance model interpretability and performance.
• Community Engagement: Share the project findings, code, and methodologies with the research
community to foster collaboration and contribute to the advancement of AI in healthcare.

1.10 Overview :
The objective of this project is to develop a deep learning-based Convolutional Neural Network (CNN)
model for the automatic classification of CT scan images into COVID-19 positive and negative categories.
This model aims to assist healthcare professionals in diagnosing COVID-19 swiftly and accurately,
leveraging AI technology to enhance diagnostic efficiency and patient care.
Methodology
1. Data Collection and Preparation:
• Gather a diverse dataset of labeled CT scan images from multiple sources, including publicly
available datasets and healthcare institutions.
• Preprocess the images to standardize their format, size, and quality, ensuring they are suitable
for model training.
2. Model Development:
• Design and implement a CNN architecture optimized for the classification of COVID-19 from
CT scans.
• Train the model using the preprocessed dataset, optimizing hyperparameters and conducting
rigorous validation to ensure robust performance.
3. Deployment and Integration:
• Develop a web application interface to facilitate the interaction between healthcare
professionals and the CNN model.
• Deploy the model on a cloud platform, ensuring scalability and accessibility for real-time
diagnosis.
4. Evaluation and Reporting:
• Evaluate the model's performance using standard metrics such as accuracy, sensitivity,
specificity, and AUC-ROC curve.
• Document the methodologies, findings, and technical aspects in a comprehensive report and
user guide for future reference.
Expected Impact
• Enhanced Diagnostic Accuracy: Provide healthcare professionals with a tool that delivers rapid and
accurate COVID-19 diagnosis from CT scans, aiding in timely patient management.
• Improved Efficiency: Reduce the time and resources required for manual interpretation of CT scans,
optimizing workflow efficiency in healthcare settings.
• Support for Public Health: Contribute to global efforts in combating the COVID-19 pandemic by
offering a scalable and reliable diagnostic solution.

1.11 Team Size

Members: 1
 2. Project Planning

1. Project Initiation
• Objective Definition: Clearly define the project objectives, scope, and expected outcomes.
• Stakeholder Identification: Identify key stakeholders, including healthcare professionals,
researchers, and IT personnel.
• Project Scope: Define the scope, requirements, and constraints of the project to ensure alignment
with stakeholders' expectations.
2. Research and Background Study
• Literature Review: Conduct a thorough review of existing literature and research on COVID-19
diagnosis using CT scans and deep learning techniques.
• Technology Exploration: Explore and evaluate deep learning frameworks (e.g., TensorFlow,
PyTorch) and cloud platforms (e.g., AWS, GCP) suitable for the project.
• Regulatory and Ethical Considerations: Review relevant healthcare regulations, data privacy laws,
and ethical guidelines for AI applications in healthcare.
3. Data Acquisition and Preparation
• Data Sources: Identify and gather a diverse dataset of CT scan images labeled as COVID-19 positive
and negative. Utilize publicly available datasets and collaborate with healthcare institutions for
additional data.
• Data Preprocessing: Preprocess the CT scan images to enhance quality, including normalization,
resizing, and data augmentation techniques.
• Data Splitting: Split the dataset into training, validation, and testing sets to train and evaluate the
CNN model.
4. Model Development
• CNN Architecture Design: Design a suitable Convolutional Neural Network (CNN) architecture for
the classification of COVID-19 from CT scan images.
• Model Implementation: Implement the CNN model using TensorFlow or PyTorch, configuring
layers, activation functions, and optimizers.
• Hyperparameter Tuning: Optimize hyperparameters (e.g., learning rate, batch size) to improve
model performance.
• Model Training: Train the CNN model on the preprocessed dataset using GPU-accelerated resources
for efficient training.
5. Model Evaluation and Validation
• Performance Metrics: Evaluate the model's performance using metrics such as accuracy, sensitivity,
specificity, and AUC-ROC curve.
• Cross-Validation: Validate the model's robustness and generalization using cross-validation
techniques.
• Visualization: Visualize evaluation metrics, confusion matrices, ROC curves, and classification
heatmaps to interpret model performance.
6. Web Application Development
• User Interface Design: Design a user-friendly web interface for healthcare professionals to interact
with the CNN model.
• Backend Integration: Integrate the trained CNN model into the web application backend using Flask
or Django frameworks.
• Deployment: Deploy the web application on a cloud platform (e.g., AWS, GCP) using Docker
containers to ensure scalability and availability.
7. Documentation and Reporting
• Technical Documentation: Document methodologies, model architecture, training processes, and
evaluation results.
• User Guide: Provide a user guide with instructions on how to use the web application, interpret
results, and troubleshoot common issues.
• Project Report: Compile a comprehensive report detailing project objectives, methodologies,
findings, and conclusions.
• Ethical Considerations: Address ethical considerations related to patient data privacy, bias
mitigation, and model transparency.
8. Project Management
• Timeline Development: Create a detailed project timeline with milestones and deliverables.
• Resource Planning: Allocate resources, including personnel, hardware, and software tools, to ensure
smooth project execution.
• Risk Management: Identify potential risks and develop mitigation strategies to minimize project
delays and disruptions.
• Communication Plan: Establish regular communication channels with stakeholders to provide
updates on project progress and address feedback.
9. Evaluation and Iteration
• Model Iteration: Iterate on the model based on feedback, performance evaluations, and new data to
continuously improve accuracy and robustness.
• Feature Enhancement: Incorporate advanced features (e.g., explainability tools, ensemble learning)
to enhance model interpretability and performance.
• Community Engagement: Share project findings, code, and methodologies with the research
community to foster collaboration and contribute to advancements in AI-driven healthcare
diagnostics.
10. Deployment and Maintenance
• Deployment Strategy: Deploy the final CNN model and web application in a production
environment.
• Performance Monitoring: Monitor the deployed system's performance, including model inference
speed, accuracy, and user interaction.
• Maintenance Plan: Establish a maintenance plan to address updates, bug fixes, and scalability issues
post-deployment.
• User Training: Provide training sessions for healthcare professionals on how to use the web
application effectively in clinical settings.
 3. Software Requirement Specification

1. Introduction
1.1 Purpose
The purpose of this document is to provide a detailed specification of the requirements for the development
of a deep learning-based Convolutional Neural Network (CNN) model and its integration into a web
application for the automatic classification of COVID-19 from CT scan images. This system aims to assist
healthcare professionals in diagnosing COVID-19 swiftly and accurately.
1.2 Scope
The system includes the development of a CNN model trained on labeled CT scan images to classify them as
COVID-19 positive or negative. It also encompasses the creation of a user-friendly web interface where
healthcare professionals can upload CT scan images and view the model's classification results. This
application will be deployed locally on a computer system.
1.3 Definitions, Acronyms, and Abbreviations
• CNN: Convolutional Neural Network
• CT: Computed Tomography
• COVID-19: Coronavirus Disease 2019
• SRS: Software Requirements Specification
1.4 References
• Project Background Document (For detailed project background and context)
• TensorFlow Documentation: https://www.tensorflow.org
• Flask Documentation: https://flask.palletsprojects.com
2. Overall Description
2.1 Product Perspective
The system consists of a CNN model for COVID-19 classification and a local web application interface. The
web application allows users to upload CT scan images and view the model's classification results.
2.2 Product Functions
• Upload CT Scan Images: Users can upload CT scan images in DICOM format to the web
application.
• View Results: Users can view the classification results, including the predicted COVID-19 status
(positive or negative).
• Visualize Images: The system provides tools to visualize uploaded CT scan images and the model's
classification outputs.
2.3 User Classes and Characteristics
• Healthcare Professionals: Users who have knowledge of medical imaging and are responsible for
diagnosing COVID-19 patients.
2.4 Operating Environment
• Web Application: Deployed locally on a computer system.
 • Backend Server: Developed using Flask framework for handling HTTP requests and model
inference.
2.5 Design and Implementation Constraints
• Performance: The CNN model should classify images in real-time or near real-time.
• Security: The system must ensure data privacy and secure handling of patient data.
2.6 Assumptions and Dependencies
• Assumptions: A sufficient amount of labeled CT scan images for training and testing the CNN model
will be available.
• Dependencies: The system depends on TensorFlow for CNN model development and Flask for web
application development.
3. Specific Requirements
3.1 External Interface Requirements
3.1.1 User Interfaces
• Upload Interface: Allows users to upload CT scan images.
• Results Interface: Displays classification results, including COVID-19 status and confidence score.
• Visualization Interface: Tools for visualizing uploaded CT scan images and model outputs.
3.1.2 Hardware Interfaces
• The system requires sufficient computational resources (CPU/GPU) for model training and inference.
3.1.3 Software Interfaces
• Backend: Developed using Python, Flask framework for handling HTTP requests and model
inference.
• Frontend: Developed using HTML, CSS, JavaScript for user interaction.
3.2 Functional Requirements
3.2.1 Upload CT Scan Images
• FR1: Users can upload CT scan images in DICOM format.
• FR2: Validate uploaded images to ensure they are in the correct format and resolution.
3.2.2 Model Classification
• FR3: The CNN model classifies uploaded CT scan images as COVID-19 positive or negative.
• FR4: Display the classification result with a confidence score.
3.2.3 Visualization
• FR5: Provide tools to visualize uploaded CT scan images and overlay classification outputs.
3.3 Non-Functional Requirements
3.3.1 Performance Requirements
• NFR1: The system should classify images in real-time or near real-time.
• NFR2: The system should handle multiple concurrent users without significant degradation in
performance.
3.3.2 Security Requirements
• NFR3: Ensure data privacy and secure handling of patient data.
3.3.3 Usability Requirements
• NFR4: The user interface should be intuitive and easy to use for healthcare professionals with varying
levels of technical expertise.
• NFR5: Provide clear and understandable error messages to assist users in troubleshooting issues.
3.4 Documentation Requirements
• DR1: Provide comprehensive documentation including project background, methodologies, technical
details, and user guides.
• DR2: Document deployment instructions and configuration settings for the web application and
backend server.
3.5 Legal and Regulatory Requirements
• LR1: The system must ensure data privacy and comply with relevant healthcare data regulations.
4. System Models
4.1 Data Model
• CT Scan Image: Represents the input data for the CNN model, stored in DICOM format.
• Classification Result: Represents the output of the CNN model, indicating COVID-19 status
(positive/negative) and confidence score.
4.2 Functional Model
• Upload Image Function: Handles the upload of CT scan images and validation.
• Model Inference Function: Performs classification of uploaded images using the CNN model.
• Visualization Function: Provides tools for visualizing images and model outputs.
5. Other Requirements
• 5.1 Performance Requirements: As specified in Section 3.3.1.
• 5.2 Safety and Security Requirements: As specified in Section 3.3.2.
• 5.3 Documentation Requirements: As specified in Section 3.4.
• 5.4 Legal and Regulatory Requirements: As specified in Section 3.5.
6. Appendices
• A. Glossary: Definitions of terms and acronyms used in the document.
• B. References: Links and citations to external resources and frameworks.
 4. Conceptual & Physical Design

Conceptual Design
1. CNN Model Architecture
• Conceptual Model:
• Input Layer: Receive CT scan images as input.
• Convolutional Layers: Extract features from the input images.
• Pooling Layers: Reduce dimensionality and down-sample the feature maps.
• Fully Connected Layers: Perform classification based on the features extracted.
• Output Layer: Provide classification results (COVID-19 positive or negative).
• Design Considerations:
• Choose an appropriate architecture (e.g., VGG, ResNet) that balances accuracy and
computational efficiency.
• Optimize hyperparameters (e.g., learning rate, batch size) for efficient training and inference.
• Implement regularization techniques (e.g., dropout) to prevent overfitting.
• Use activation functions (e.g., ReLU) for non-linearity.
2. Web Application Interface
• Conceptual Model:
• Upload Interface: Allows users to upload CT scan images.
• Result Display: Shows classification results and confidence scores.
• Visualization Tools: Tools to visualize CT scan images and model outputs.
• Design Considerations:
• Ensure the interface is intuitive and easy to use for healthcare professionals.
• Implement error handling and feedback mechanisms for user interactions.
• Design for responsiveness and compatibility across different devices and browsers.

Physical Design
1. CNN Model Implementation
• Physical Model:
• Framework: Implement the CNN model using TensorFlow or PyTorch.
• Development Environment: Use Python for coding and debugging.
• GPU Acceleration: Utilize GPUs (e.g., NVIDIA CUDA) for faster training and inference.
• Design Considerations:
• Develop modular code for model architecture and training pipeline.
• Use version control (e.g., Git) for managing code changes and collaboration.
• Document code and include comments for clarity and maintainability.
2. Web Application Implementation
• Physical Model:
• Backend: Develop the backend using Flask framework in Python.
• Frontend: Use HTML, CSS, and JavaScript for the user interface.
• Deployment: Deploy locally on your computer system.
• Design Considerations:
• Ensure secure communication between frontend and backend using HTTPS.
• Handle file uploads and manage storage of CT scan images.
• Integrate the CNN model for real-time or near real-time inference.
• Test and optimize application performance for local deployment.
 5. System Testing

1. Testing Strategy
The testing strategy for your system will encompass several types of testing to ensure comprehensive
coverage:
• Unit Testing: Test individual components of the CNN model (layers, modules) and backend
functionalities (API endpoints, data handling).
• Integration Testing: Verify interactions between different components (web interface, backend
server, CNN model).
• System Testing: Test the system as a whole to ensure it meets the specified requirements.
• Performance Testing: Measure the system's responsiveness and stability under varying loads.
• Usability Testing: Evaluate the user interface for ease of use and intuitiveness.
2. Test Scenarios
2.1. Unit Testing
• CNN Model:
• Scenario 1: Test each layer (convolutional, pooling, fully connected).
• Scenario 2: Verify input and output dimensions with JPEG images.
• Scenario 3: Validate activation functions (e.g., ReLU) with JPEG images.
• Backend Server:
• Scenario 4: Test API endpoints for JPEG image upload, inference, and result retrieval.
• Scenario 5: Verify data handling and preprocessing for JPEG images.
2.2. Integration Testing
• Scenario 6: Upload a JPEG CT scan image and verify that it is processed correctly through the entire
pipeline (web interface -> backend -> CNN model).
• Scenario 7: Test simultaneous uploads from multiple users to ensure system stability and performance
with JPEG images.
2.3. System Testing
• Scenario 8: Upload multiple JPEG CT scan images of varying sizes and verify that classification
results are accurate and timely.
• Scenario 9: Test the system with edge cases (e.g., corrupt files, unusually large images) to ensure
robustness with JPEG images.
2.4. Performance Testing
• Scenario 10: Measure the time taken for JPEG image upload, processing, inference, and result
display.
• Scenario 11: Test the system under load with multiple concurrent users to ensure responsiveness and
stability with JPEG images.
2.5. Usability Testing
• Scenario 12: Evaluate the user interface for clarity, ease of navigation, and accessibility with JPEG
images.
 • Scenario 13: Validate error handling and user feedback mechanisms with JPEG images.
3. Test Procedures
3.1. Pre-requisites
• Ensure that the development environment is set up correctly with all dependencies installed.
• Prepare a test dataset of JPEG CT scan images with known classifications (COVID-19
positive/negative).
3.2. Test Execution
• Unit Testing:
• Run unit tests for each component and module using JPEG images.
• Use testing frameworks (e.g., pytest for Python) to automate tests.
• Integration Testing:
• Execute integration tests to verify interactions between components with JPEG images.
• Check that data flows correctly through the system.
• System Testing:
• Perform end-to-end tests using the test dataset of JPEG images.
• Evaluate system behavior and accuracy of classification results.
• Performance Testing:
• Measure system performance under different conditions (normal load, peak load) with JPEG
images.
• Use tools like Apache JMeter or locust.io for load testing.
• Usability Testing:
• Conduct usability tests with stakeholders (healthcare professionals) using JPEG images.
• Gather feedback on the user interface and overall user experience.
3.3. Test Evaluation
• Document test results, including any failures or issues encountered.
• Verify that the system meets all functional and non-functional requirements with JPEG images.
• Address and resolve any identified defects or performance bottlenecks.
4. Acceptance Criteria
• All unit tests pass successfully with JPEG images.
• Integration tests verify correct interactions between components with JPEG images.
• System tests demonstrate accurate classification of JPEG CT scan images.
• Performance tests show that the system meets response time and throughput requirements with JPEG
images.
• Usability tests confirm that the user interface is intuitive and user-friendly with JPEG images.
 6. Summary of experience gained

During the development and implementation of the deep learning-based CNN model
and web application for COVID-19 classification from JPEG format CT scan images,
several key experiences and insights were gained:
1. Deep Learning and CNN Model Development:
• Experience: Developed a deep learning-based CNN model architecture tailored for COVID-
19 classification from CT scan images.
• Insights: Understanding the importance of model architecture selection, hyperparameter
tuning, and training strategies for achieving high accuracy.
2. Web Application Development:
• Experience: Implemented a web application for uploading JPEG format CT scan images,
processing them using the CNN model, and displaying classification results.
• Insights: Importance of frontend design for usability and backend integration for seamless
data flow.
3. Testing and Validation:
• Experience: Conducted various types of testing including unit testing, integration testing,
system testing, performance testing, and usability testing.
• Insights: The significance of comprehensive testing to validate system functionality,
performance, and user experience.
4. Challenges Faced:
• Experience: Encountered challenges such as dataset availability, model training time, and
system integration.
• Insights: Overcoming challenges through collaboration, problem-solving, and adapting
methodologies to suit project needs.
5. Project Management:
• Experience: Managed project timelines, milestones, and resources effectively.
• Insights: Importance of planning, communication, and agile development methodologies to
deliver the project on schedule.
6. Impact and Significance:
• Experience: Contributed to the field of medical diagnostics by providing a tool for automated
COVID-19 classification.
• Insights: Understanding the potential impact of AI-driven diagnostics in healthcare,
particularly during pandemics.
7. Future Directions:
• Experience: Identified areas for future research and improvement, such as expanding the
dataset, enhancing model accuracy, and integrating with healthcare systems.
 • Insights: The ongoing evolution of AI technologies in healthcare and the potential for
continued innovation in diagnostic tools.
8. Personal and Professional Growth:
• Experience: Enhanced technical skills in deep learning, web development, and software
testing.
• Insights: Continuous learning and adaptation to new technologies are essential for staying
competitive in the field of AI and healthcare informatics.
 7. Conclusion

In conclusion, the development of the deep learning-based CNN model and web application for COVID-19 classification
from JPEG format CT scan images has been a significant endeavor with several key takeaways and achievements.
Achievements:
1. Successful Model Development: We successfully developed a convolutional neural network (CNN)
model architecture specifically designed for the classification of COVID-19 cases from CT scan
images in JPEG format. The model demonstrated robust performance in distinguishing COVID-19
positive cases from other types of lung infections.
2. Integration with Web Application: The CNN model was integrated seamlessly into a web
application, allowing users to upload JPEG CT scan images for automatic classification. This
integration provided a user-friendly interface for healthcare professionals to obtain rapid diagnostic
insights.
3. Comprehensive Testing: We conducted comprehensive testing across multiple dimensions, including
unit testing, integration testing, system testing, performance testing, and usability testing. This
rigorous testing approach ensured the reliability, accuracy, and usability of the system.
4. Performance Evaluation: The system's performance was evaluated under various conditions,
demonstrating its ability to handle real-world scenarios and deliver accurate classification results
within a reasonable timeframe.
5. Contribution to Healthcare: This project contributes to the ongoing efforts in leveraging AI for
medical diagnostics, particularly in the context of infectious diseases like COVID-19. The developed
system can potentially aid healthcare professionals in making timely and accurate decisions, thereby
improving patient outcomes.
Key Takeaways:
1. Technical Insights: The project provided valuable insights into deep learning model development,
web application integration, and the importance of comprehensive testing methodologies in ensuring
system reliability and performance.
2. Challenges Overcome: Throughout the project, we encountered and successfully addressed
challenges such as dataset management, model optimization, and integration complexities. These
experiences have strengthened our problem-solving abilities and technical skills.
3. Future Directions: Looking forward, there are opportunities for further enhancement and expansion
of the system. This includes refining the CNN model with additional data sources, integrating with
electronic health records (EHR) systems for seamless patient management, and adapting the system
for other infectious diseases.
Impact and Future Potential:
The impact of this project extends beyond its immediate application. It underscores the potential of AI-driven
diagnostics in transforming healthcare delivery, particularly in times of global health crises. The lessons
learned and the experiences gained will guide future endeavors in AI and healthcare informatics, contributing
to advancements in medical technology and patient care.

Closing Thoughts
In closing, the development of this deep learning-based COVID-19 classification system represents a
significant step towards harnessing AI for public health challenges. We are proud of the achievements made
and look forward to continuing our efforts in advancing the field of medical diagnostics through innovation
and collaboration.

8. Scope of development
 • CNN Model Development: Design and optimize a convolutional neural network (CNN) architecture
specifically for classifying COVID-19 cases from JPEG format CT scan images.
• Web Application Development: Implement a user-friendly web application to facilitate the
uploading of CT scan images, processing through the CNN model, and displaying classification
results.
• Testing and Validation: Conduct rigorous testing, including unit testing, integration testing, system
testing, performance testing, and usability testing, to ensure the reliability and accuracy of the system.
• Deployment: Deploy the system on a local computer environment for demonstration and testing
purposes.
• Documentation: Prepare comprehensive documentation, including technical specifications, user
manuals, and installation guides, to support future maintenance and enhancements.

Limitations
While the deep learning-based COVID-19 classification system has achieved significant milestones, several
limitations were identified during the development and testing phases:
• Dataset Limitations: The availability and size of the COVID-19 CT scan image dataset were limited,
which may impact the generalizability of the CNN model.
• Model Performance: Despite rigorous optimization, the CNN model's performance may vary
depending on the quality and resolution of the CT scan images.
• Processing Time: The processing time for image classification may vary based on the hardware
specifications and computational resources available.
• User Interface: The current version of the web application has basic functionalities and may require
further enhancements to improve user experience and interface design.

Future Enhancements
To enhance the deep learning-based COVID-19 classification system, the following future enhancements are
recommended:
• Dataset Expansion: Incorporate a larger and more diverse dataset of COVID-19 CT scan images to
improve the CNN model's accuracy and generalizability.
• Model Refinement: Continuously refine the CNN model by experimenting with different
architectures, hyperparameters, and training techniques to achieve higher classification accuracy.
• Integration with Healthcare Systems: Integrate the system with electronic health records (EHR) and
hospital information systems (HIS) to facilitate seamless data exchange and patient management.
• Real-Time Processing: Implement real-time image processing capabilities to enable immediate
classification and diagnosis of COVID-19 cases.
• AI-driven Decision Support: Develop AI-driven decision support tools to assist healthcare
professionals in interpreting classification results and making informed decisions.
 • User Interface Improvements: Enhance the web application's user interface with intuitive features,
such as image visualization tools, patient data management, and interactive reporting.
• Performance Optimization: Optimize the system's performance to handle larger datasets and
increased user traffic, ensuring scalability and responsiveness.
• Research Collaboration: Collaborate with research institutions and healthcare organizations to
validate the system's performance and contribute to the advancement of medical diagnostics.

9. References

www.kaggle.com
www.tensorflow.com
10. I/O Screen