Application of machine learning methods in Mammography image processing in the classification of Image Compression of Breast Cancer

Cancer is one of the major health concerns in the world. One of them is breast cancer, which is the most common type of cancer among women, and death from breast cancer has the highest rate among other cancers for women. Therefore, the goal of auto-cancer detection applications is to classify histopathological images into distinct patterns and to divide them into benign and malignant types by tissue analysis. And one of the main problems in these programs is the complexity of histological images. Many researches have been done to analyze and diagnose histopathological images and automatically classify breast cancer images using intelligent computer applications. Due to the importance of breast cancer image processing, the set of operations and processes that are performed in image analysis in various fields, is done through image processing that can reduce or increase the image quality. The method of this study is the use of machine learning methods in mammography image processing in the classification of image compression of breast cancer to examine and classify the foci of risk in the images of breasts related to cancer patients. In addition, a new method based on mammographic image processing is presented and according to the proposed model, the masses are completely separated from other parts of the image and their quality and amount of brightness is increased so that the location and size of the mass in a mammographic image are clear and with Specify high accuracy. The proposed method is very effective in reducing human error in detecting lumps in images. The results showed that the proposed intelligent system can process and classify image compression of breast cancer in mammographic images without human error with very high accuracy.

کلیدواژه ها:

Breast Cancer ، Mean Squared Error (MSE) ، Peak Signal to Noise Ratio (PSNR) ، Compression Rate