Deep Learning Technique for Classification of Breast Cancer using Ultrasound Images

Breast cancer is a significant medical and social issue that draws attention from the global scientific community. The classification of ultrasound images of breast cancer plays a crucial role in computer-aided diagnostic systems. In this paper, we present a deep learning technique for the diagnosis of breast cancer using ultrasound images. The proposed system leverages Transfer Learning, a machine learning approach that allows the reuse of pre-trained models for new tasks. Four pre-trained models, namely VGG16, VGG19, MobileNetV2, and ResNet50V2, were considered for building the system. Our study uses a public dataset that contains 9016 ultrasound images related to benign and malignant breast cancers. Results indicate that the VGG19 network is the most accurate model in classifying breast cancer as benign or malignant. The proposed system showed remarkable results, with an accuracy rate of 98.44% as obtained from the VGG19 model. This study emphasizes the ability of Transfer Learning to improve the precision of breast tumor classification based on Ultrasound Images.