One of the most common types of cancer is breast cancer. Pathological image processing of the breast has emerged as a significant method for early breast cancer diagnosis. In the field of medical image diagnosis, the use of medical image processing to help doctors detect potential breast cancer as soon as possible has always been a hot topic. In this paper, a bosom disease acknowledgment strategy in light of picture handling is efficiently explained from four perspectives: Image fusion, image segmentation, image registration, and breast cancer detection in the context of breast cancer examination, the accomplishments and application scope of supervised learning, unsupervised learning, deep learning, CNN, and other methods are discussed. The possibility of unaided learning and move learning for bosom malignant growth conclusion is prospected. Finally, patients with breast cancer should have their privacy protected.
HTML PDFShare this article
Archives of Surgical Oncology received 37 citations as per Google Scholar report
