Machine Learning in Healthcare: Breast Cancer Detection Using Graph Convolutional Network

Machine learning is a vast field of research. The idea is to build a machine learning-based cad system for breast cancer detection using mammogram image data. At first, we use supervised classification techniques in our mammogram image data and then feed the classified data into the GCN model for detection. We investigated that the GCN model can give better accuracy than traditional machine learning models. Breast cancer is one of the most common cancers that women suffer the most. But breast cancer can be detected early. The vast amount of research shows that if breast cancer is successfully detected early, the patient life can be 99% saved early. A screening mammogram is the other most useful thing in the detection of breast cancer. According to researchers, with the help of mammograms breast cancer can be detected three years earlier before the start of cancer symptoms. Graph Convolutional Neural Network (GCN) is a new field of convolutional machine learning. Unlike CNN, GCN follows a non-Euclidian approach which can show better results in image classification. We aim to investigate the GCN model into breast cancer mammogram image data, that it can give better accuracy than traditional machine learning models. After evaluating our proposed GCN algorithm to four others, we discovered that GCN achieved the accuracy of 81 percent.