Breast Cancer Classification in Ultrasound Images using Transfer Learning
Ahmed Hijab, Muhammad Ali Rushdi, Mohammed Mohammed Mohammed Gomaa, Ayman Eldeib · 2019
Computer-aided detection of malignant breast tumors in ultrasound images has been receiving growing attention. In this paper, we propose a deep learning methodology to tackle this problem. The training data, which contains several hundred images of benign and malignant cases, was used to train a deep convolutional neural network (CNN). Three training approaches are proposed: a baseline approach where the CNN architecture is trained from scratch, a transfer-learning approach where the pre-trained VGG16 CNN architecture is further trained with the ultrasound images, and a fine-tuned learning approach where the deep learning parameters are fine-tuned to overcome overfitting. The experimental results demonstrate that the fine-tuned model had the best performance (0.97 accuracy, 0.98 AUC), with pre-training on US images. Creating pre-trained models using medical imaging data would certainly improve deep learning outcomes in biomedical applications.