An Effective Development of Breast Cancer Identification Methodology Using Elevated Neural Classification Procedure

In which MIAS mammography Image dataset, it has been found that breast cancer detection and diagnosis can be examined. The huge collection of mammograms on the dataset can be used by the researchers to perform advanced segmentation, which gives promising qualitative outcomes and better classification accuracy. However, these methods are able to identify cancerous areas shown by the suggested model which shows the original image along with its segmented binary mask and tumor boundaries. Such a thorough analysis demonstrates why reliable data is so crucial in medical imaging, and allows for more progress on breast cancer screening knowledge including developing earlier detection techniques and patient outcomes in the future. The performance metrics of the proposed ENCP approach are then compared with the implementation algorithms of the previous classification machine learning models. On top, our proposed ENCP provides the best accuracy of 98.6%, which is much better than the previous machine learning models.