[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119489-en":3,"doc-seo-119489-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119489,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Pneumonia Disease Detection Using Chest X-Rays and Machine Learning - research paper","Pneumonia is a high-mortality respiratory infection driven by microorganisms and environmental factors, where timely diagnosis and treatment are essential to prevent rapid deterioration. Chest X-rays are widely accessible and cost-effective but require expert interpretation, making automated detection a valuable alternative. The study builds an early pneumonia detection pipeline using machine learning with image processing and deep learning, training a CNN from scratch and fine-tuning a pretrained ResNet-50 on a large RSNA dataset. It evaluates dataset size and augmentation effects and compares models with existing work using accuracy, precision, recall, and F1.","algorithms   \nArticle  \nPneumonia Disease Detection Using Chest X-Rays and Machine Learning  \nCathryn Usman 1, Saeed Ur Rehman 1, Anwar Ali 2,*, Adil Mehmood Khan 1 and Baseer Ahmad 1  \nAcademic Editor: Antonio Della Cioppa  \nReceived: 30 October 2024  \nRevised: 10 December 2024  \nAccepted: 17 December 2024  \nPublished: 3 February 2025  \nCitation: Usman, C.; Rehman, S.U.; Ali, A.; Khan, A.M.; Ahmad, B. Pneumonia Disease Detection Using Chest X-Rays and Machine Learning. Algorithms 2025, 18, 82. [https://](https://)[ ](https://)[doi.org/10.3390/a18020082](doi.org/10.3390/a18020082)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Faculty of Science and Engineering, University of Hull, Hull HU6 7RX, UK;  \n[c.m.usman-2022@hull.ac.uk](c.m.usman-2022@hull.ac.uk) (C.U.); [s.rehman2@hull.ac.uk](s.rehman2@hull.ac.uk) (S.U.R.); [a.m.khan@hull.ac.uk](a.m.khan@hull.ac.uk) (A.M.K.);  \n[baseer.ahmad@hull.ac.uk](baseer.ahmad@hull.ac.uk) (B.A.)  \n2 Department of Electronic and Electrical Engineering, Swansea University Bay Campus, Swansea SA1 8EN, UK  \n* Correspondence: [anwar.ali@swansea.ac.uk](anwar.ali@swansea.ac.uk)  \nAbstract: 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.  \nKeywords: machine learning; CNN; RESNET; pneumonia infections  \n1. Introduction and Background  \nPneumonia is a respiratory infection caused by bacteria, viruses, and fungi [1], leading to inflammation of the lungs, with symptoms including fever, muscle aches, coughing and breathing difficulties [2] . If not treated on time, pneumonia can be fatal. Each year, more than four million people die from pneumonia [3] . Antibiotics and antivirals are drugs that can treat pneumonia-caused viruses or bacteria; even so, the ability to diagnose and treat pneumonia early on is necessary for the best possible patient care [4] .  \n","cbCaiqf6g6yl3pun","https://ap.wps.com/l/cbCaiqf6g6yl3pun","pdf",3383954,1,14,"English","en",105,"# Abstract\n# Introduction and Background\n## Pneumonia and clinical impact\n## Chest X-ray for diagnosis\n## Machine learning and deep learning for CAD\n## CNNs and transfer learning","[{\"question\":\"Why is early pneumonia detection important in this research?\",\"answer\":\"Pneumonia can become fatal if not treated on time. Early detection and treatment are emphasized to avoid severe outcomes.\"},{\"question\":\"What data and learning approaches are used to detect pneumonia?\",\"answer\":\"The study uses the RSNA pneumonia detection challenge dataset with 26,684 chest images and builds a machine learning solution using a CNN trained from scratch and a pretrained ResNet-50 with deep learning.\"},{\"question\":\"How are the models evaluated and what results were reported?\",\"answer\":\"Models are compared using accuracy, precision, recall, and F1 score. The best reported accuracy came from the scratch CNN without augmentation (accuracy 0.79), while the pretrained model showed better generalizability despite lower overall accuracy.\"}]","Pneumonia Disease Detection Using Chest X-Rays and Machine Learning - research paper | PDF",1785724584,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"pneumonia-disease-detection-using-chest-x-rays-and-machine-learning-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/pneumonia-disease-detection-using-chest-x-rays-and-machine-learning-research-paper/119489/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early pneumonia detection important in this research?","Question",{"text":75,"@type":76},"Pneumonia can become fatal if not treated on time. Early detection and treatment are emphasized to avoid severe outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and learning approaches are used to detect pneumonia?",{"text":80,"@type":76},"The study uses the RSNA pneumonia detection challenge dataset with 26,684 chest images and builds a machine learning solution using a CNN trained from scratch and a pretrained ResNet-50 with deep learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and what results were reported?",{"text":84,"@type":76},"Models are compared using accuracy, precision, recall, and F1 score. 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