Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; Singh, Anju; Singh, Divakar; Shoran, Preety; Shankar, Uma; Yadav, Meenakshi; Jain, Rituraj Title A name given to the resource Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived ARO-The Scientific Journal of Koya University;Volume;13;Issue;1;Article No.;12174;pp.215-226 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14500/aro.12174" target="_blank" rel="noreferrer noopener">https://doi.org/10.14500/aro.12174</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011745503?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011745503?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Singh A., Department of Computer Science and Engineering, Lakshmi Narain College of Technology, Kalchuri Nagar, Raisen Road, Madhya Pradesh, Bhopal, India; Singh D., Department of Computer Science and Engineering, university Institute of Technology, Barkatullah University, Madhya Pradesh, Bhopal, India; Shoran P., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR Campus, Ghaziabad, India; Shankar U., Department of Management, Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq; Yadav M., Department of Information Technology, Galgotias College of Engineering and Technology, Greater Noida, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India Description An account of the resource Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. 2025 Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav and Rituraj Jain. Subject The topic of the resource Artificial intelligence; Chest X-rays; Fuzzy opponent histogram filter; Machine learning; Pneumonia Publisher An entity responsible for making the resource available Koya University Relation A related resource ISSN: 24109355; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online