Diagnosing Covid-19 using AI based Medical Image Analysis
Pages 204 - 212
Abstract
The pandemic of COVID-19 is currently one of the most significant problems being dealt with, all around the world. It mainly affects the lungs of the infected person which can further result in serious threats. So to avoid this life threatening condition, we have used chest radiological images for COVID-19 detection. This infectious disease is communicable and is spreading rapidly throughout the world. Hence, fast and accurate detection of COVID-19 is mandatory, so one can be given proper treatment well before time. In this paper, the proposed work aims to develop a web application, namely CovSADs(Covid-19 Smart A.I. Diagnosis System), using deep learning approach for faster and efficient detection of COVID-19. This web application uses X-ray and CT scan images for the evaluation. Here, we have developed DeepCovX and DeepCovCT models by incorporating Transfer Learning (TL) approach for COVID-19 detection via chest X-ray and CT scan images respectively. Further, we have used GradCam in case of X-ray to make sure our model is looking at relevant information to make decisions and image-segmentation is used in case of CT scan to extract and localize Region-of-interest (ROI) from binary image. Our proposed models show the accuracy of 95.89% and 98.01% for X-ray and CT scan images respectively. We have obtained specificity of 99.57%, sensitivity of 100%, and AUC of 0.998 in case of X-ray and specificity of 98.80%, sensitivity of 97.06%, and AUC of 0.9875 in case of CT scan images. F1-score is obtained as 0.98 for COVID-19 and 0.98 for Non-COVID-19 in case of CT scan images. Both quantitative and qualitative results demonstrate promising results for COVID-19 detection and extraction of infected lung regions. The primary objective of the web application is to assist the radiologists not only for mass screening but also to help in planning treatment process.
References
[1]
2020. https://sirm.org/category/senza-categoria/covid-19/
[2]
2020. Chest Imaging. Retrieved March 28, 2020 from https://twitter.com/ChestImaging/status/1243928581983670272
[3]
2020. International committee on taxonomy of viruses (ICTV). Retrieved February 14, 2020 from https://talk.ictvonline.org/
[4]
2020. Radiopaedia Database. Retrieved April 5, 2020 from https://radiopaedia.org/articles/COVID-19-3?lang=
[5]
2020. World health organization (WHO). Retrieved February 15, 2020 from https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200213-sitrep-24-covid 19.pdf?sfvrsn=9a7406a4_4
[6]
Md Zahangir Alom, Shaifur Rahman, Shamima Nasrin, Tarek M. Taha, and Vijayan K. Asari. 2020. COVID-MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches. arxiv:2004.03747
[7]
A. Bhandary, G. Prabhu, Rajinikanth, Thanaraj, Satapathy, Robbins, Shasky, Zhang, Tavares, and Raja. 2020. Deep-Learning Framework to Detect Lung Abnormality-A Study with Chest X-ray and Lung CT Scan Images. Pattern Recognition Letters 129 (2020), 271–8. https://doi.org/10.1016/j.patrec.2019.11.013
[8]
Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, and M. T. Islam. 2020. Can AI help in screening Viral and COVID-19 pneumonia?IEEE Access 8(2020), 132665 – 132676. https://doi.org/10.1109/ACCESS.2020.3010287
[9]
Fernandes, Rajinikanth, and Kadry. 2019. A Hybrid Framework to Evaluate Breast Abnormality using Infrared Thermal Images. IEEE Consumer Electronics Magazine 2019 8, 5 (2019), 31–36. https://doi.org/10.1109/MCE.2019.2923926
[10]
Ezz El-Din Hemdan, Shouman Marwa, and EsmailKarar Mohamed. 2020. COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images. arxiv:2003.11055
[11]
Ioannis and Mpesiana. 2020. Covid-19: Automatic Detection from X-ray Images Utilizing Transfer Learning with Convolutional Neural Networks. Physical and engineering sciences in medicine 43, 2 (2020), 635–640. https://doi.org/10.1007/s13246-020-00865-4
[12]
Shahin Khobahi, Chirag Agarwal, and Mojtaba Soltanalian. 2020. CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images. medrxiv:10.1101/2020.04.14.20065722
[13]
Xin Li, Chengyin Li, and Dongxiao Zhu. 2020. COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays. arxiv:2004.03042
[14]
Wang Linda, Zhong Qiu Lin, and Alexander Wong. 2020. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. Scientific Reports 10(2020), 19549. https://doi.org/10.1038/s41598-020-76550-z
[15]
Minaee, Kafieh, Sonka, Yazdani, and Jamalipour Soufi. 2020. Deep-COVID: Predicting COVID-19 from Chest X-ray images using Deep Transfer Learning. Medical Image Analysis 65 (2020), 101794. https://doi.org/10.1016/j.media.2020.101794
[16]
Paul Mooney. 2020. Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
[17]
Ali Narin, Ceren Kaya, and Ziynet Pamuk. 2020. Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks. arxiv:2003.10849
[18]
Diego Oliva, Erik Cuevas, GonzaloPajares, Daniel Zaldivar, and Marco Perez-Cisneros. 2013. Multilevel Thresholding Segmentation Based on Harmony Search Optimization. Journal of Applied Mathematics(2013), 575414. https://doi.org/10.1155/2013/575414
[19]
N. Otsu. 1979. A threshold selection method from gray-level histograms. . IEEE Transactions on Systems, Man, and Cybernetics (1979), 62–66. https://doi.org/10.1109/TSMC.1979.4310076
[20]
Afshar P., Heidarian S., Naderkhani F., Oikonomou A., Plataniotis K. N., and Mohammadi A. 2020. COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images. Pattern recognition letters 138 (2020), 638–643. https://doi.org/10.1016/j.patrec.2020.09.010
[21]
Joseph Paul Cohen, Morrison Paul, and Dao Lan. 2020. COVID-19 Image Data Collection. arxiv:2003.11597https://github.com/ieee8023/covid-chestxray-dataset
[22]
Tawsifur Rahman, Muhammad Chowdhury, and Amith Khandakar. 2020. Radiography Database. https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/data
[23]
V. Rajinikanth, Nilanjan Dey, Alex Noel Joseph Raj, Aboul Ella Hassanien, K. C. Santosh, and Nadaradjane Sri Madhava Raja. 2020. Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images. arxiv:2004.03431
[24]
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. . Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017), 618–626. https://doi.org/10.1109/ICCV.2017.74
[25]
Rahman T, Khandakar A, Qiblawey Y, Tahir, Kiranyaz, Abul Kashem, Islam, Maadeed S, Zughaier SM, Khan MS, and Chowdhury. 2021. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med 132(2021), 104319. https://doi.org/10.1016/j.compbiomed.2021.104319
[26]
Ucar and Korkmaz. 2020. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based Diagnosis of the Coronavirus Disease 2019 (COVID-19) from X-ray Images. Medical Hypotheses 140(2020), 109761. https://doi.org/10.1016/j.mehy.2020.109761
[27]
Xingyi Yang, Xuehai He, Jinyu Zhao, Yichen Zhang, Shanghang Zhang, and Pengtao Xie. 2020. COVID-CT-Dataset: A CT Scan Dataset about COVID-19. https://doi.org/UCSD-AI4H/COVID-CT arxiv:2003.13865
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Published In
January 2022
357 pages
ISBN:9781450385824
DOI:10.1145/3493700
Copyright © 2022 ACM.
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Published: 08 January 2022
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CODS-COMAD 2022
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CODS-COMAD 2022: 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)
January 8 - 10, 2022
Bangalore, India
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