Description

Breast cancer is the most common cancer among Malaysian women and roughly one in 19 women at risk of breast cancer in Malaysia. The number of breast cancer cases is steadily growing especially with increasing number of ageing population. The screening practice using mammography needs to be better and potentially efficient. There is always room for advancement when it comes to medical imaging. Early detection of cancer can reduce the risk of deaths for cancer patients. The objective of this paper is to compare the breast cancer detection with two model networks of deep learning technique. The overall process involves image preprocessing, classification and performance evaluation. In this paper, we evaluate the performance of deep learning model network which are VGG16 and ResNet50 to classify between normal tumor and abnormal tumor using IRMA dataset. The result show that VGG16 produces the better result with 94% compared to ResNet50 with 91.7% in term of accuracy.