Identification of Breast Cancer using Deep Learning Algorithm with Mammography Images

Women are drawn to cancer, the world's most dangerous disease. Thus, our practical goal should be to cure cancer through scientific research, followed by early cancer detection to help eliminate it. We found few cancer detection methods in 41 papers. This research proposes a Deep Learning method convolutional brain organization for diagnosing breast cancer using Mammography MIAS data. Using MIAS Dataset, the research shows how deep learning can diagnose breast cancer. First, we gathered the dataset and used pre-processing technique for scaled and channel information. Then, we divided the dataset into preparation and testing and created a few charts for representation. Execute model on the dataset again and achieve 98. This database has 200 photos and 12 highlights. This report uses 12 breast cancer diagnosis highlights from pre-processing. We employed Watershed Segmentation, Color-based segmentation, and Adaptive Mean Filters to scaled datasets before applying the model and achieving exactness. In this research, we contrast profound learning calculations with different man-made intelligence calculations and find that our system performs well.