Enhanced Deep Learning Frameworks for Breast Cancer Detection: Addressing Data Imbalance, Adaptability, and Computational Efficiency

Baasireddy Thirumala Veera Mamatha, Boddu Munikumar

Bommapathi Gayathri, Keesaram Venkatesh

(Final Year B.Tech Students)

B. Sireesha

 (Assistant Professor),

Department of Information Technology

Sri Venkateswara College of Engineering (SVCE), Tirupati, India

Abstract-Breast cancer diagnosis from mammogram scans is challenged by the scarcity of balanced datasets and the complexity of medical image interpretation. Existing systems predominantly utilize a deep learning framework combining ResNet50 for feature extraction and the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. While these methods achieve high accuracy on balanced datasets (99%) and reasonable results on imbalanced data (90%), they depend heavily on pre-trained models like VGG16 and ResNet50, which limits adaptability to diverse imaging modalities. Moreover, computational demands and synthetic data generation methods such as SMOTE may not fully capture real-world variability, constraining deployment in resource-limited settings and affecting robustness. To address these limitations, the proposed system introduces an enhanced deep learning architecture incorporating domain-specific pretraining, lightweight model design, and advanced data augmentation techniques beyond SMOTE. This framework aims to improve generalization, computational efficiency, and interpretability through novel visualization tools. The benefits include more accurate, reliable, and accessible breast cancer classification across diverse datasets and clinical environments, offering a promising advancement for early detection and diagnostic support in breast cancer care.