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 Conference Paper Faculty Publications- Conference Papers 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 Tanni, Nikita; Elappila, Manu; George, Tessa; Premjith, Nithin Title A name given to the resource Enhancing Pneumonia Diagnosis with Convolutional Neural Networks: A Comprehensive Evaluation 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 Lecture Notes in Networks and Systems;Volume;5589 LNNS;pp.597-609 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-1687-9_42" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1687-9_42</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010198719?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010198719?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 Tanni N., CHRIST University, Bengaluru, India; Elappila M., CHRIST University, Bengaluru, India; George T., CHRIST University, Bengaluru, India; Premjith N., CHRIST University, Bengaluru, India Description An account of the resource Pneumonia, a significant health concern globally, presents unique challenges in diagnosis and treatment due to its diverse ethology and impact on respiratory function. The potential of augmentation techniques and Convolutional Neural Networks, for automated pneumonia detection is explored in this study. Employing a transfer learning approach with VGG16, DenseNet, and our proposed model achieves outstanding accuracy (95%) and robust performance metrics. The research explores augmentation techniques to enhance the precision and accuracy of the model, emphasizing the importance of data augmentation in improving classification accuracy. A comparative analysis with related models highlights advancements in automated pneumonia detection, showcasing the efficacy of our proposed model. The models ability to correctly identify pneumonia from chest X-ray pictures is demonstrated by the results, suggesting that medical image analysis could benefit from practical implementation of this model. Future directions include expanding the dataset, exploring alternative architectures, and integrating explanation techniques to enhance model interpretability. This research contributes to the advancement of artificial intelligence in healthcare, offering a promising approach for accurate and efficient pneumonia diagnosis, thus addressing critical challenges in respiratory medicine. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Accuracy; Advance healthcare; Automated classification; Convolutional Neural Networks (CNNs); DenseNet; Machine learning; VGG16 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981961686-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online