A Deep Learning Framework for Multi-Class Classification of Breast Cancer Abnormalities
R. Subasini, Devi Vijayan · 2024
Breast cancer, impacting both men and women, though it is notably more prevalent in women, ranks among the most widespread types of cancer worldwide. Early detection of breast cancer significantly improves treatment outcomes and increases the likelihood of successful recovery, underscoring the importance of regular screenings and prompt medical attention upon noticing any changes or abnormalities in breast health. Utilizing a Computer-Aided Diagnosis (CAD) system further enhances early detection capabilities, assisting clinicians in accurate diagnosis and treatment planning. Unlike existing binary methods, the proposed method employs a three-class classification system distinguishing between mass, micro calcification, and normal tissues. Initial preprocessing is followed by patch extraction and augmentation, playing a pivotal role in mitigating overfitting and enabling the model to learn robust features that generalize effectively to new medical images. A deep-learning architecture, with various pre-trained models includes R$e$sNet50, ResNetl0l, ResNetl52, Inception ResNet V2, Inception V3, VGG-16, and VGG-19, is trained on the augmented dataset to classify patches into normal, micro calcification, and mass categories. The proposed model is validated on the combined dataset which demonstrates the model's accuracy in diagnosing breast abnormalities, with Inception ResN et V2 achieving an accuracy of 97.93 %, outperforming other pre-trained models. The system achieved an overall sensitivity, specificity, precision, and Fl-score approximate 0.9411, 0.9885, 0.9740 and 0.9557, respectively, illustrating the model's robustness and its ability to reliably detect and classify breast abnormalities.