The comparison of CNN based networks on infiltrating ductal carcinoma images classification in the medical application field

Abstract

Breast cancer is common in women, ranking first in the incidence of cancer in women and occupying first place in the mortality rate of cancer in women. Because of the seriousness of breast cancer, researchers and institutions worldwide are making unremitting efforts to find the perfect diagnostic and therapeutic solutions. The increasing maturity of image processing technology has led to the growing use of computer-based pathological diagnosis in diagnosing various diseases, and researchers have done much research on this. This paper presents some studies on breast cancer histopathological images based on hematoxylin-eosin staining. Currently, the diagnosis of breast cancer is based on hematoxylin-eosinstained histopathological images. First, the surgeon will take a piece of tissue from the patient's lesion and make a histological section. Next, the pathologist will observe the histological section and diagnose the results. In this way of diagnosis, the patient's diagnosis depends more on the subjective judgment of the pathologist, which requires a high degree of professionalism and is not very efficient. Therefore, for hematoxylin-eosin-stained breast cancer histopathology images, there is a need for a computer-assisted automatic diagnosis method that can reduce the pathologist's burden and make the patient's diagnosis objective and efficient with the help of image processing technology. To this end, this paper compares the performance of three standard machine learning algorithms for comparing hematoxylin-eosin-stained breast cancer histopathology images.