Machine Learning Based Mammogram Classification for Breast Cancer Diagnosis Using Neural Networks

Among females, breast cancer is high as a major killer. Breast cancer is easily diagnosed when anomalies are spotted in their earliest stages. Accurately diagnosing breast cancer and treating patients as soon as possible will be facilitated by effective diagnostic technologies. Experiments were performed to determine if breast cancers were benign or malignant using data from the Wisconsin Diagnosis Breast Cancer database. To do this, we employ the supervised learning algorithm Support Vector Machine (SVM) with kernels such as Linear and Neural Networks (NN). Comparing the models' results reveals that the Neural Network technique is more "accurate" and "precise" than the Support Vector Machine in the categorization of breast cancer and appears to be a quick and efficient method.