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Breast cancer is a tumor disease with a high incidence of female diseases, but it is also a disease that can reduce the mortality rate through early diagnosis and early treatment. A method of gray level co-occurrence matrix combined with BP neural network is proposed to improve the recognition rate of breast tumors. Using computer intelligent method to detect whether breast tumors are benign or malignant is a pattern recognition problem of breast microscopic images, which can help diagnose diseases in advance, thus improving the tumor treatment effect. How to accurately diagnose breast cancer patients is the key to the prevention and early diagnosis of breast tumors. Ultrasound imaging technology plays an important role in the diagnosis and treatment of breast tumors because of its characteristics of no radiation damage, simplicity, effectiveness and low cost. The traditional ultrasonic image segmentation method usually performs the most basic segmentation function of the image, and the subsequent analysis work often depends on the manual operation of doctors or technicians. The interpretation and diagnosis of near-infrared breast images are subjective, and doctors inevitably have subjective differences in interpretation. In order to reduce the subjective influence of different doctors on the analysis results and reduce the pressure of doctors' work, this paper innovatively proposes an automatic segmentation method of ultrasonic images by combining the target recognition algorithms in the field of pattern recognition. In this paper, the medical images of breast cancer tumor cells are researched, and based on BP neural network, the segmentation scheme of breast cancer medical images and the detection scheme of tumor cells based on feature scale expansion are proposed, which can successfully realize the automatic segmentation of breast cancer ultrasound images, effectively identify the tumor areas in the images, have good stability and accuracy, and bring new ideas for the application of BP neural network diagnosis system.
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