Novel mammography images approach for breast cancer diagnosis using ensemble feature extraction
Breast cancer detection; Ensemble feature selection; Machine learning; Mammography
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).
Supriya, U.; Vadivu, M. Senthil; Gowdhamkumar, S.; Rajarajeswari, P.; Umesh, R.; Gunapriya, D.
AIP Conference Proceedings;Volume;3306;Issue;1;Article No.;40016;
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https://doi.org/10.1063/5.0275833 https://www.scopus.com/pages/publications/105008866203?origin=resultslist
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