research-article

Conventional Machine Learning based on Feature Engineering for Detecting Pneumonia from Chest X-rays

Authors:

Theophilus Ansah-Narh,

Emmanuel Proven-Adzri,

Marcellin AtemkengAuthors Info & Claims

SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020

Pages 149 - 155

https://doi.org/10.1145/3410886.3410898

Published: 11 September 2020 Publication History

Abstract

Chest X-ray is the standard approach used to diagnose pneumonia and other chest diseases. Early diagnosis of the disease is very relevant in the life of people, but analyzing X-ray images can be complicated and needs the competence of a radiographer. In this paper, we demonstrate the potential of detecting the disease in chest X-rays using conventional machine learning classifiers. The principal component analysis is used for the data dimensionality reduction and features extraction then the extracted features are used to train several model classifiers. We obtained an accuracy of, using of the principal explained variance.

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Vishwakarma DBhandari HAgrawal NKriti Rawat J(2023)Application of Deep Learning Techniques for Pneumonia Detection Using Chest X-Ray ImagesAdvancements in Bio-Medical Image Processing and Authentication in Telemedicine10.4018/978-1-6684-6957-6.ch010(185-200)Online publication date: 17-Mar-2023
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Conventional Machine Learning based on Feature Engineering for Detecting Pneumonia from Chest X-rays
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image ACM Other conferences

SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020

September 2020

258 pages

ISBN:9781450388474

DOI:10.1145/3410886

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 September 2020

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SAICSIT '20

SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020

September 14 - 16, 2020

Cape Town, South Africa

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Cited By

Nhlapho WAtemkeng MBrima YNdogmo J(2024)Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI ImagesInformation10.3390/info1504018215:4(182)Online publication date: 27-Mar-2024
https://doi.org/10.3390/info15040182
Vishwakarma DBhandari HAgrawal NKriti Rawat J(2023)Application of Deep Learning Techniques for Pneumonia Detection Using Chest X-Ray ImagesAdvancements in Bio-Medical Image Processing and Authentication in Telemedicine10.4018/978-1-6684-6957-6.ch010(185-200)Online publication date: 17-Mar-2023
https://doi.org/10.4018/978-1-6684-6957-6.ch010
Montalcini CVoelkl BGómez YGantner MToscano M(2022)Evaluation of an Active LF Tracking System and Data Processing Methods for Livestock Precision Farming in the Poultry SectorSensors10.3390/s2202065922:2(659)Online publication date: 15-Jan-2022
https://doi.org/10.3390/s22020659
Brima YAtemkeng MTankio Djiokap SEbiele JTchakounté F(2021)Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray ImagesDiagnostics10.3390/diagnostics1108148011:8(1480)Online publication date: 16-Aug-2021
https://doi.org/10.3390/diagnostics11081480
Radiuk PBarmak OKrak I(2021)An Approach to Early Diagnosis of Pneumonia on Individual Radiographs based on the CNN Information TechnologyThe Open Bioinformatics Journal10.2174/187503620211401009314:1(93-107)Online publication date: 19-Nov-2021
https://doi.org/10.2174/1875036202114010093

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