Breast Cancer Analytics Classification using MEFBUD DCNN Techniques
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Abstract
Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. There are numerous procedures and approaches for detecting cancer in the tissues of the breast. This work presents the image processing, segmentation, and deep learning methodologies and approaches for the diagnosis of breast cancer. This research will help people make better decisions and use trustworthy techniques to find breast cancer early enough to save a woman's life. Pre-processing, segmentation, and classification are some of this system's steps. We've included a thorough study of several techniques or processes, along with information on how they're used and how performance is measured. The stated results lead to the conclusion that, in order to increase the chances of surviving breast cancer, it is crucial to develop new procedures or techniques for early diagnosis. For researchers to effectively diagnose breast cancer, segmentation and classification phases are also difficult. Therefore, the precise diagnosis and categorization of breast cancer still require the use of more advanced equipment and techniques.
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References
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