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 Prakash, Shubhra; Ramamurthy, B. Title A name given to the resource Texture-Based DNN for Pneumonia in Thorax X-Rays 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 International Research Journal of Multidisciplinary Scope;Volume;6;Issue;3;pp.1333-1349 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.47857/irjms.2025.v06i03.05118" target="_blank" rel="noreferrer noopener">https://doi.org/10.47857/irjms.2025.v06i03.05118</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012840237?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012840237?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 Prakash S., CHRIST University, Bangalore, India; Ramamurthy B., CHRIST University, Bangalore, India Description An account of the resource This paper introduces an innovative methodology for identifying pneumonia in thoracic X-ray images through the application of neural network classifiers. In our experiment, we employed a comprehensive training regimen involving multiple neural network classifiers, each trained on distinct sets of texture features meticulously extracted from thoracic X-ray images. Four different gray-level matrices and a neighboring gray-tone difference matrix (NGTDM) were used to generate these input features, guaranteeing a reliable depiction of the textural properties found in the X-ray pictures. We carried out an extensive evaluation utilizing a number of performance criteria to gauge the trained classifiers' efficacy. Classifying the thoracic X-ray pictures into two groups pneumonia and healthy state was the assignment assigned to the classifiers. A thorough study of the classifiers' performance was provided by our assessment measures, which comprised accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). The experimental findings showed that the suggested method accomplished a remarkable 91% overall test categorization accuracy, which was encouraging. This degree of precision highlights how well our approach works to accurately diagnose pneumonia from thoracic X-ray images. Furthermore, the consistent performance across different metrics highlights the robustness and generalizability of the proposed strategy. 2025, Iquz Galaxy Publisher. All rights reserved. Subject The topic of the resource GLCM; GLDM; GLRLM; GLSZM; NGTDM; Texture Features Publisher An entity responsible for making the resource available Iquz Galaxy Publisher Relation A related resource ISSN: 2582631X; 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 Format The file format, physical medium, or dimensions of the resource online