No Cover Image

Journal article 374 views 77 downloads

Pneumonia Disease Detection Using Chest X-Rays and Machine Learning

Cathryn Usman, Saeed Ur Rehman Orcid Logo, Anwar Ali Orcid Logo, Adil Mehmood Khan, Baseer Ahmad

Algorithms, Volume: 18, Issue: 2, Start page: 82

Swansea University Author: Anwar Ali Orcid Logo

  • 70057.pdf

    PDF | Version of Record

    © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

    Download (3.23MB)

Check full text

DOI (Published version): 10.3390/a18020082

Abstract

Pneumonia is a deadly disease affecting millions worldwide, caused by microorganisms and environmental factors. It leads to lung fluid build-up, making breathing difficult, and is a leading cause of death. Early detection and treatment are crucial for preventing severe outcomes. Chest X-rays are com...

Full description

Published in: Algorithms
ISSN: 1999-4893
Published: MDPI AG 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70057
first_indexed 2025-07-30T12:00:18Z
last_indexed 2025-08-14T05:39:44Z
id cronfa70057
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-08-13T16:13:20.9454024</datestamp><bib-version>v2</bib-version><id>70057</id><entry>2025-07-30</entry><title>Pneumonia Disease Detection Using Chest X-Rays and Machine Learning</title><swanseaauthors><author><sid>f206105e1de57bebba0fd04fe9870779</sid><ORCID>0000-0001-7366-9002</ORCID><firstname>Anwar</firstname><surname>Ali</surname><name>Anwar Ali</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-07-30</date><deptcode>ACEM</deptcode><abstract>Pneumonia is a deadly disease affecting millions worldwide, caused by microorganisms and environmental factors. It leads to lung fluid build-up, making breathing difficult, and is a leading cause of death. Early detection and treatment are crucial for preventing severe outcomes. Chest X-rays are commonly used for diagnoses due to their accessibility and low costs; however, detecting pneumonia through X-rays is challenging. Automated methods are needed, and machine learning can solve complex computer vision problems in medical imaging. This research develops a robust machine learning model for the early detection of pneumonia using chest X-rays, leveraging advanced image processing techniques and deep learning algorithms that accurately identify pneumonia patterns, enabling prompt diagnosis and treatment. The research develops a CNN model from the ground up and a ResNet-50 pretrained model This study uses the RSNA pneumonia detection challenge original dataset comprising 26,684 chest array images collected from unique patients (56% male, 44% females) to build a machine learning model for the early detection of pneumonia. The data are made up of pneumonia (31.6%) and non-pneumonia (68.8%), providing an effective foundation for the model training and evaluation. A reduced size of the dataset was used to examine the impact of data size and both versions were tested with and without the use of augmentation. The models were compared with existing works, the model&#x2019;s effectiveness in detecting pneumonia was compared with one another, and the impact of augmentation and the dataset size on the performance of the models was examined. The overall best accuracy achieved was that of the CNN model from scratch, with no augmentation, an accuracy of 0.79, a precision of 0.76, a recall of 0.73, and an F1 score of 0.74. However, the pretrained model, with lower overall accuracy, was found to be more generalizable.</abstract><type>Journal Article</type><journal>Algorithms</journal><volume>18</volume><journalNumber>2</journalNumber><paginationStart>82</paginationStart><paginationEnd/><publisher>MDPI AG</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1999-4893</issnElectronic><keywords>machine learning; CNN; RESNET; pneumonia infections</keywords><publishedDay>3</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-02-03</publishedDate><doi>10.3390/a18020082</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace, Civil, Electrical, and Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>ACEM</DepartmentCode><institution>Swansea University</institution><apcterm>Other</apcterm><funders>This research received no external funding.</funders><projectreference/><lastEdited>2025-08-13T16:13:20.9454024</lastEdited><Created>2025-07-30T12:58:20.5750043</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering</level></path><authors><author><firstname>Cathryn</firstname><surname>Usman</surname><order>1</order></author><author><firstname>Saeed Ur</firstname><surname>Rehman</surname><orcid>0009-0009-4566-7144</orcid><order>2</order></author><author><firstname>Anwar</firstname><surname>Ali</surname><orcid>0000-0001-7366-9002</orcid><order>3</order></author><author><firstname>Adil Mehmood</firstname><surname>Khan</surname><order>4</order></author><author><firstname>Baseer</firstname><surname>Ahmad</surname><order>5</order></author></authors><documents><document><filename>70057__34870__4ee9a156dde64639bba47c54e9908e8b.pdf</filename><originalFilename>70057.pdf</originalFilename><uploaded>2025-07-30T13:00:09.9790366</uploaded><type>Output</type><contentLength>3383954</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-08-13T16:13:20.9454024 v2 70057 2025-07-30 Pneumonia Disease Detection Using Chest X-Rays and Machine Learning f206105e1de57bebba0fd04fe9870779 0000-0001-7366-9002 Anwar Ali Anwar Ali true false 2025-07-30 ACEM Pneumonia is a deadly disease affecting millions worldwide, caused by microorganisms and environmental factors. It leads to lung fluid build-up, making breathing difficult, and is a leading cause of death. Early detection and treatment are crucial for preventing severe outcomes. Chest X-rays are commonly used for diagnoses due to their accessibility and low costs; however, detecting pneumonia through X-rays is challenging. Automated methods are needed, and machine learning can solve complex computer vision problems in medical imaging. This research develops a robust machine learning model for the early detection of pneumonia using chest X-rays, leveraging advanced image processing techniques and deep learning algorithms that accurately identify pneumonia patterns, enabling prompt diagnosis and treatment. The research develops a CNN model from the ground up and a ResNet-50 pretrained model This study uses the RSNA pneumonia detection challenge original dataset comprising 26,684 chest array images collected from unique patients (56% male, 44% females) to build a machine learning model for the early detection of pneumonia. The data are made up of pneumonia (31.6%) and non-pneumonia (68.8%), providing an effective foundation for the model training and evaluation. A reduced size of the dataset was used to examine the impact of data size and both versions were tested with and without the use of augmentation. The models were compared with existing works, the model’s effectiveness in detecting pneumonia was compared with one another, and the impact of augmentation and the dataset size on the performance of the models was examined. The overall best accuracy achieved was that of the CNN model from scratch, with no augmentation, an accuracy of 0.79, a precision of 0.76, a recall of 0.73, and an F1 score of 0.74. However, the pretrained model, with lower overall accuracy, was found to be more generalizable. Journal Article Algorithms 18 2 82 MDPI AG 1999-4893 machine learning; CNN; RESNET; pneumonia infections 3 2 2025 2025-02-03 10.3390/a18020082 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Other This research received no external funding. 2025-08-13T16:13:20.9454024 2025-07-30T12:58:20.5750043 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Cathryn Usman 1 Saeed Ur Rehman 0009-0009-4566-7144 2 Anwar Ali 0000-0001-7366-9002 3 Adil Mehmood Khan 4 Baseer Ahmad 5 70057__34870__4ee9a156dde64639bba47c54e9908e8b.pdf 70057.pdf 2025-07-30T13:00:09.9790366 Output 3383954 application/pdf Version of Record true © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
spellingShingle Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
Anwar Ali
title_short Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
title_full Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
title_fullStr Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
title_full_unstemmed Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
title_sort Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
author_id_str_mv f206105e1de57bebba0fd04fe9870779
author_id_fullname_str_mv f206105e1de57bebba0fd04fe9870779_***_Anwar Ali
author Anwar Ali
author2 Cathryn Usman
Saeed Ur Rehman
Anwar Ali
Adil Mehmood Khan
Baseer Ahmad
format Journal article
container_title Algorithms
container_volume 18
container_issue 2
container_start_page 82
publishDate 2025
institution Swansea University
issn 1999-4893
doi_str_mv 10.3390/a18020082
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
document_store_str 1
active_str 0
description Pneumonia is a deadly disease affecting millions worldwide, caused by microorganisms and environmental factors. It leads to lung fluid build-up, making breathing difficult, and is a leading cause of death. Early detection and treatment are crucial for preventing severe outcomes. Chest X-rays are commonly used for diagnoses due to their accessibility and low costs; however, detecting pneumonia through X-rays is challenging. Automated methods are needed, and machine learning can solve complex computer vision problems in medical imaging. This research develops a robust machine learning model for the early detection of pneumonia using chest X-rays, leveraging advanced image processing techniques and deep learning algorithms that accurately identify pneumonia patterns, enabling prompt diagnosis and treatment. The research develops a CNN model from the ground up and a ResNet-50 pretrained model This study uses the RSNA pneumonia detection challenge original dataset comprising 26,684 chest array images collected from unique patients (56% male, 44% females) to build a machine learning model for the early detection of pneumonia. The data are made up of pneumonia (31.6%) and non-pneumonia (68.8%), providing an effective foundation for the model training and evaluation. A reduced size of the dataset was used to examine the impact of data size and both versions were tested with and without the use of augmentation. The models were compared with existing works, the model’s effectiveness in detecting pneumonia was compared with one another, and the impact of augmentation and the dataset size on the performance of the models was examined. The overall best accuracy achieved was that of the CNN model from scratch, with no augmentation, an accuracy of 0.79, a precision of 0.76, a recall of 0.73, and an F1 score of 0.74. However, the pretrained model, with lower overall accuracy, was found to be more generalizable.
published_date 2025-02-03T05:25:08Z
_version_ 1851460061336961024
score 11.089572

Similar Items