Classification of Breast Cancer Mammographic Images Using A Light-Weighted Convolutional Neural Network

Deep learning is a method demanded by radiologists to assist them interpret and classify medical images correctly. A Convolutional Neural Network (CNN) is the most widely used method for classifying and analysing images. In this paper, a light-weighted CNN is presented for breast cancer classification using a dataset of breast mammography images. The suggested methodology improves the classification of mammary cancer images to assist radiologists in the detection of mammary cancer. The application of the proposed model can help in the diagnosis of mammary cancer using digital mammograms without any preceding information about the existence of a cancerous lesion. The proposed CNN can categorize the input medical images as malignant or benign with an accuracy of 99.35% which is the highest accuracy achieved for such a large mammography dataset.