A Novel Transfer Learning-Based Model for Ultrasound Breast Cancer Image Classification

Breast cancer is the second most dangerous disease for women after lung cancer. As in most diseases, an early detection and the corresponding treatment of breast cancer increase the survival rate of patients. An automated system for detection of breast cancer is required as manual techniques are time consuming and expensive. In this study, we have proposed a novel transfer learning model that can classify an ultrasound of the breast as either normal, benign, or malignant with high accuracy. The proposed method uses a modified ResNet50 model trained initially on ImageNet dataset and further on the breast cancer ultrasound dataset (BUSI). We have added custom layers at the head of our model which are able to extract features from ultrasound images. Using the model described in this paper, we have achieved 97.8% accuracy in detecting breast cancer, a recall of 97.68%, precision of 99.21% and 98.44% F1-score. This deep learning model can be implemented as a component of an existing medical diagnosis system or deployed as a stand-alone system. Using our model for breast cancer diagnosis can result in decreased diagnosis time compared to traditional means and hence ensure that patients receive an early treatment for their illness.