Automatic breast cancer detection and classification using deep learning techniques

Breast cancer is considered to be the main cause of loss of life in women of every nation. The quick identification of any changes in normal breast helps in diagnosing the breast cancer by radiologist in a short span of time. Appropriate breast cancer diagnostic device with high efficiency will assist the health care Physicians to monitor the prognosis and treatment in a timely manner. In our work, we used Wisconsin Diagnosis Breast Cancer database to classify begin and malignant forms of breast cancer. Supervised studying algorithms like-Support Vector Machine (SVM) along with kernels like Linear and Neural Networks (NN) were used for comparison. The performances of these models were analysed the place Neural Network method gives high „accuracy‟ and „precision‟ in contrast to the Support Vector Machine, and seems to be quick and efficient method in the diagnostic classification of breast cancer. © 2020 Mattingley Publishing. All rights reserved.