An Explainable AI-Based Transfer Learning Method for Breast Cancer Prediction

Breast cancer identification via ultrasound images requires high accuracy and transparency to assist clinicians in taking appropriate decisions. This work demonstrates a deep learning system for classification of breast ultrasound images into benign, malignant, and normal images using the EfficientNet-B0 architecture fine-tuned on the Breast Ultrasound Identification (BUSI) dataset. To mitigate class imbalance and stabilize the network, data augmentation including random horizontal flipping, rotation, and color jittering is applied. Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to generate visual explanations by identifying regions of interest such as tumor margins and texture patterns. The model achieved an average accuracy of 99%, demonstrating high efficacy in lesion detection. The integration of Explainable AI (XAI) not only improves diagnostic confidence but also bridges the gap between clinical practice and AI. The results prove the potential for combining high-performing deep learning models with interpretability methods in developing reliable breast cancer diagnosis tools suitable for actual clinical practice.