Research Article
Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models
@INPROCEEDINGS{10.1007/978-3-031-08878-0_11, author={Tat-Bao-Thien Nguyen and Minh-Vuong Ngo and Van-Phong Nguyen}, title={Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models}, proceedings={Industrial Networks and Intelligent Systems. 8th EAI International Conference, INISCOM 2022, Virtual Event, April 21--22, 2022, Proceedings}, proceedings_a={INISCOM}, year={2022}, month={6}, keywords={Machine learning Multi-layer perceptron Convolutional Neutral Network (CNN) Biomedical image classification EfficientNet}, doi={10.1007/978-3-031-08878-0_11} }- Tat-Bao-Thien Nguyen
Minh-Vuong Ngo
Van-Phong Nguyen
Year: 2022
Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models
INISCOM
Springer
DOI: 10.1007/978-3-031-08878-0_11
Abstract
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 EficientNet 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.
