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According to some medical imaging techniques, breast histopathology imagescalled Hematoxylin and Eosin are considered as the gold standard for cancerdiagnoses. Based on the idea of dividing the pathologic image (WSI) intomultiple patches, we used the window [512,512] sliding from left to right andsliding from top to bottom, each sliding step overlapping by 50% to augmenteddata on a dataset of 400 images which were gathered from the ICIAR 2018 GrandChallenge. Then use the EffficientNet model to classify and identify thehistopathological images of breast cancer into 4 types: Normal, Benign,Carcinoma, Invasive Carcinoma. The EffficientNet model is a recently developedmodel that uniformly scales the width, depth, and resolution of the networkwith a set of fixed scaling factors that are well suited for training imageswith high resolution. And the results of this model give a rather competitiveclassification efficiency, achieving 98% accuracy on the training set and 93%on the evaluation set.

Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models