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