Breast Cancer Classification in Ultrasound Images Using Two-Phase EfficientNetB7 Transfer Learning
TL;DR
A systematic but innovative two phases transfer learning’sbased deep learning classification framework is developed using popular EfficientNetB7 architecture architecture for breast cancer classification which encompasses formalisation into components like data augmentation, progressive fine-tuning and adaptive learning rate optimization as methods for model generalisation.
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.