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 Supriya, U.; Vadivu, M. Senthil; Gowdhamkumar, S.; Rajarajeswari, P.; Umesh, R.; Gunapriya, D. Title A name given to the resource Novel mammography images approach for breast cancer diagnosis using ensemble feature extraction 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 AIP Conference Proceedings;Volume;3306;Issue;1;Article No.;40016; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0275833" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0275833</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008866203?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008866203?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 Supriya U., Acculer Media Technologies Pvt Ltd, Tamil Nadu, Coimbatore, India; Vadivu M.S., Department of statistics and Data Science, Christ University, Tamil Nadu, Bengaluru, India; Gowdhamkumar S., Department of Training, PSG Industrial Institute (PSGCT), Peelamedu, Tamil Nadu, Coimbatore, India; Rajarajeswari P., Department of Artificial Intelligence and Data science, Erode Sengunthar Engineering College, Perundurai, Tamil Nadu, Erode, India; Umesh R., Department of Information Technology, Velammal College of Engineering and Technology, Tamil Nadu, Madurai, India; Gunapriya D., Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, India Description An account of the resource By using ensemble feature extraction methods to mammography pictures, this study introduces a novel strategy for the early detection of breast cancer. Beginning with preprocessing stages that use data augmentation to improve the dataset, the technique incorporates a methodical flowchart. Following the creation and individual training of an ensemble model that incorporates CNN architectures like as DenseNet, AlexNet, and i-Alex, the final model attains an impressive level of accuracy. Optimized feature vectors are the end result of a process that begins with feature fusion and continues with dimensionality reduction methods like principal component analysis (PCA). Utilizing LASSO and ReliefF for feature selection helps to refine the collection of features, which in turn improves accuracy metrics. Utilizing cross-validated hyperparameter optimization, classifier training showcases the effectiveness of SVM, Random Forest, and XGBoost. The ensemble method is clearly better according to the performance assessment, which takes into account sensitivity, specificity, F1-score, and AUC. Integrating the chosen classifier into a mammography screening system ensures clinical interpretability by providing clear visualizations. Updating the model with fresh data on a regular basis and doing continuous monitoring ensure that it remains accurate. By working together in the clinic and taking radiologists' comments into account, we can improve the system's performance and reveal its capabilities as a cutting-edge instrument for accurate breast cancer detection. 2025 Author(s). Subject The topic of the resource Breast cancer detection; Ensemble feature selection; Machine learning; Mammography 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