Breast Cancer: Automatic Detection from Mammogram Images by Utilizing Deep Learning Methods

The second most common malignancy among women is breast cancer. A lifetime risk of developing breast cancer is one in eight women. And men account for about 1% of all cases. Breast cancer develops when healthy breast cells multiply uncontrollably, expand abnormally, and cluster together. Early identification is among the most excellent strategies to prevent breast cancer. Today, properly trained machine learning algorithms can be widely applied in industries like surveillance, medicine, and data management to find answers to issues without known responses or where the existing ones are insufficient. This research compares two widely used machine and deep learning algorithms. The proposed model was evaluated using the Mammographic Image Analysis Society (MIAS) database as a training set. To compare the effectiveness of various machine learning techniques in regard to essential factors, including accuracy, precision, recall, and f1-score. Using the CNN algorithm, we achieved 99% accuracy with 99% precision, sensitivity, and f1-score. Therefore, our technique has great promise for future clinical advancement.