Classification of Breast Cancer Malignancy Using Machine Learning Mechanisms in TensorFlow and Keras

Classification of breast cancer malignancy using digital mammograms remains a difficult task in breast cancer diagnosis and plays a key role in early detection of breast cancer. Inspired by rapid progress in the field of artificial intelligence, we explored several machine learning mechanisms, i.e., Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Deep Neural Network (DNN) given in TensorFlow and Keras deep learning frameworks, and used Python programming to predict if a patient case is malignant or benign. This retrospective study was based on the Breast Cancer Wisconsin (Diagnostic) Data Set that contains a set of 30 features, e.g., radius mean, texture mean, perimeter mean, etc., previously extracted from digital mammograms. In addition, breast cancer diagnosis results were provided as the gold standard for training and testing of the machine learning mechanisms. Based on verification results on the test set, the five machine learning mechanisms achieved the sensitivity of 94.4%, 94.4%, 91.9%, 90.8%, 98.5%, and the specificity of 92.7%, 90.5%, 92.3%, 94.6%, 91.1%, respectively. In conclusion, our machine learning mechanisms achieved competitive performance results with the state-of-art techniques presented by other researchers and may provide useful second opinion to radiologists in breast cancer diagnosis.