Breast Cancer Classification in Ultrasound Images Using Two-Phase EfficientNetB7 Transfer Learning

Abstract

Breast cancer is a leading cause of cancer morbidity and mortality among women globally, emphasizing the need for accurate and timely diagnostic methods. A systematic but innovative two phases transfer learning based deep learning classification framework is developed using popular EfficientNetB7 architecture architecture for breast cancer classification. Breast ultrasound imaging proves a high degree of complexity to extract features with limited available medical datasets to address it and the proposed methodology attributes to it. We develop a framework which encompasses formalisation into components like data augmentation, progressive fine-tuning and adaptive learning rate optimization as methods for model generalisation. Experiments on the Breast Ultrasound Images (BUSI) dataset show that the model achieves best accuracy of over 91. 25% when classifying breast lesions into benign, malignant, and normal. It shows good discriminative abilities (validation accuracy: 93.62%) and a trained model converge well. We compare our method with the current ones and show significant advancements over all previous methods making our approach suitable for computer-aided diagnosis systems in clinical workflows.