Abstract
Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the field of medical image diagnosis. In this paper, a breast cancer recognition method based on image processing is systematically expounded from four aspects: breast cancer detection, image segmentation, image registration, and image fusion. The achievements and application scope of supervised learning, unsupervised learning, deep learning, CNN, and so on in breast cancer examination are expounded. The prospect of unsupervised learning and transfer learning for breast cancer diagnosis is prospected. Finally, the privacy protection of breast cancer patients is put forward.
Highlights
Breast cancer is one of the most common malignant tumors
The types of medical imaging widely used in clinic include X-ray imaging (X-CT), magnetic resonance imaging (MRI), nuclear medicine imaging (NMI), and ultrasonic imaging (UI)
Accumulation of deep learning in image processing can not be directly transferred to breast cancer image processing
Summary
Breast cancer is one of the most common malignant tumors. According to Chinese Women’s survey, breast cancer is the most common malignant tumor in Chinese women, and the incidence rate is increasing year by year. Because of the huge amount of data and the poor imaging features of early breast cancer, early diagnosis is very difficult. With the development of image processing technology and early diagnosis technology, image processing of breast pathology has become an important way of early diagnosis of breast cancer, which mainly includes the study of masses, calcifications, and breast density. The tumor and normal tissue were separated according to the extracted features. Another manifestation of breast cancer on X-ray images is a large breast density [1]. The types of medical imaging widely used in clinic include X-ray imaging (X-CT), magnetic resonance imaging (MRI), nuclear medicine imaging (NMI), and ultrasonic imaging (UI). In addition to clinical diagnosis, medical image processing plays an important auxiliary role in medical teaching, operation planning, operation simulation, and various medical researches [5, 6]
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