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 Kumar, S. Babu; Kumar, M. Vinoth Title A name given to the resource Detection and classification of lung cancer using deep neural network 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 Journal of Medical Engineering and Informatics;Volume;17;Issue;5;pp.429-443 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJMEI.2025.148638" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJMEI.2025.148638</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017018666?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017018666?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 Kumar S.B., Department of AI, ML &amp; DS, CHRIST University, Karnataka, Bangalore, India; Kumar M.V., Department of Information Science and Engineering, RV Institute of Technology and Management, Karnataka, Bangalore, India, Visvesvaraya Technological University, Karnataka, Belagavi, 590018, India Description An account of the resource Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%. Copyright 2025 Inderscience Enterprises Ltd. Subject The topic of the resource DCNN; deep convolution neural network; EfficientNetB7; histopathology; lung cancer Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17550653; 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 Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online