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 Babu Kumar, S.; Vinoth Kumar, M. Title A name given to the resource Lung cancer detection and classification with optimal feature selection and two-fold-deep-learning-classifiers 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 Multimedia Tools and Applications;Volume;84;Issue;37;pp.46021-46063 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11042-025-20947-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11042-025-20947-3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008376695?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008376695?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 Babu Kumar S., Assistant Professor, Department of AI, ML &amp; DS, CHRIST University, Karnataka, Bangalore, India; Vinoth Kumar M., Associate Professor, Department of Information Science and Engineering, RV Institute of Technology and Management, Karnataka, Bangalore, India Description An account of the resource The respiratory system is undoubtedly hampered by lung disorders. Also, one of the important reasons for death among people all around the world has been lung cancer. Early discovery can advance human survival probabilities. As a result, a unique ensemble-deep-learning paradigm for lung cancer detection and classification is established in the present research effort. The projected model includes five major phases: (a) image augmentation, (b) pre-processing, (c) segmentation, (d) feature extraction, (e) feature selection, and (f) lung cancer detection and classification, respectively. The collected raw CT images are augmentation with SMOTE. The augmented images are pre-processed via Median Filtering (for noise removal) and Contrast-limited adaptive histogram equalization (CLAHE) (for image contrast enhancement). Subsequently, from the pre-processed data, the ROI is identified via optimized U-NETS. The activation function (hyper-parameter) of U-NETS is optimized via a new hybrid optimization model-Digging Tunaswarm Optimizer (DTO). This DTO is the conceptual amalgamation of two standard meta-heuristic optimization models, namely Honey Badger Algorithm (HBA) and Tuna Swarm Optimization (TSO) models, respectively. Then, from the selected ROI area, the features like texture features (Manhattan Distance-based-GLCM, GLRM), Color features (Color Histogram), and Shape features (Moments, Area, Perimeter) are extracted. Among the extracted features, the optimal features are selected using DTO. This optimal feature selection reduces the computational complexity of the projected model. Finally, using these extracted optimal features, the two-fold-deep-learning-classifier framework is trained. This two-fold-deep-learning-classifiers framework encapsulates the Bidirectional long-short term memory (Bidirectional LSTM) and the Recurrent Neural Network (RNN) and the Modified Convolutional Neural Network (M-CNN). In the first phase, the Bi-LSTM and RNN are clamped, and they are trained with the selected optimal factors. The outcome from Bi-LSTM and also RNN was fed as input to M-CNN. Final detected findings based on the existence or absence of lung cancer are acquired from the M-CNN, whose loss function has been modified with RMSE. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model. The proposed model has a higher overall accuracy (92.4%) detecting modelling accuracy (96.3%) and classification accuracy (92.4%) compared to other models such as HBA, TSO, CNN, 3D CenterNet, and TSCNN. The use of a two-fold deep learning framework is responsible for these improvements, and the model also has lower failure rates (FPR and FNR) in detecting lung cancer. It is suggested that the proposed approach is effective in early-stage lung cancer detection. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. Subject The topic of the resource CT images; DTO; Lung cancer detection; Manhattan Distance-based-GLCM; Optimized U-NETS; M-CNN Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 13807501; CODEN: MTAPF 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