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 Veerabhaktula, Ashish Joseph; Sathiyamurthy, Babu Kumar; Kumar, Kukatlapalli Pradeep; Lingaraju, Anoop Ganadalu; Rangegowda, Nagarathna Chevvenahalli; Channegowda, Santhrupth Bundanoor Title A name given to the resource High-precision lung disease detection and classification from chest radiographs using deep and ensemble neural networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3345;Issue;1;Article No.;20191; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0298542" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0298542</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028279629?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028279629?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 Veerabhaktula A.J., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Sathiyamurthy B.K., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Lingaraju A.G., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India; Rangegowda N.C., Department of Artificial Intelligence and Machine Learning, BNM Institute of Technology), Karnataka, Bengaluru, India; Channegowda S.B., Department of Computer Science and Engineering, CHRIST University, Karnataka, Bengaluru, India Description An account of the resource Chest X-rays are a quick and effective way to diagnose lung diseases. This research developed deep learning models to automatically detect chest X-rays of COVID-19, normal, and viral pneumonia patients. The goal was to evaluate deep learning for automated detection of lung diseases from chest X-rays. The research implemented transfer learning with ResNet101 and EfficientNetB0 architectures using a public chest x-ray database with over 21,000 images across COVID-19, normal, and other pneumonia infection classes. Pretrained ImageNet weights were used to initialize the models before fine-tuning them to classify features in chest X-rays. Data augmentation techniques like rotation, shifting, and flipping were applied to expand the number and diversity of training images. The models achieved exceptional performance with accuracy scores of 93.7% for ResNet101 and 95.3% for EfficientNetB0 on test data. Additionally, an Ensemble model, the combination of the two models, was implemented, achieving an accuracy of 96.4%. The findings demonstrate the capability of Ensemble deep convolutional neural networks for accurate automated classification of chest X-rays for Lung disease. Through data augmentation and transfer learning, high-precision models were developed without needing exceedingly sizeable medical image datasets. These deep learning classifiers could serve as rapid diagnostic decision support systems to identify potential lung disease patients using readily available chest X-rays. Such tools could assist healthcare providers, especially when access to expensive diagnostic tests is limited. 2026 Author(s). Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; 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