Authors - Piyush Sharma, Harish Patidar, Anuj Kumar Abstract - This research introduces a ResNet-based framework for multiclass classification of mammographic density and mass regions. The framework was rigorously tested using two prominent mammographic datasets, INbreast and DDSM, and benchmarked against other models, including CNNs, Random Forest (RF), Support Vector Machines (SVMs), Logistic Regression (LR), and K-Nearest Neighbors (KNN). ResNet demonstrated superior performance across all critical evaluation metrics—accuracy, precision, recall, F1-score, and AUC—outclassing the comparative models on both datasets. Its proficiency in extracting complex hierarchical features and addressing multiclass classification tasks positions it as a robust choice for breast cancer diagnosis. This framework offers a reliable and efficient tool for automating diagnostic processes, with the potential to significantly improve clinical decision-making and patient care.
