An Analysis of Deep Learning Models for Breast Cancer Mammography Image Classification

Breast cancer is the most common and fastest-growing cancer on the planet. Breast cancer is more common in women than in men. Breast cancer can be managed if it is detected early and treated properly. Mammography is one of the procedures for detecting breast cancer. A mammogram is an X-ray scan of the breast that can help a radiologist detect early signs of breast cancer. Advances in the field of machine learning have resulted in increasingly intelligent and self-sufficient computer-aided diagnostic (CAD) systems as the learning ability of ML approaches has improved. Recent improvements in machine learning (ML) have a considerable impact on enhancing diagnostic skills, with deeper and more extensive representation approaches, usually referred to as deep learning (DL) approaches. Several studies have been conducted to identify the breast cancer. This research compares six optimized deep learning algorithms for identifying breast cancer in mammography pictures, namely InceptionV3, VGG16, ResNet50, ResNet101, DenseNet169, and Mobile Net, and finds that InceptionV3 beats the others.