Cervical Intraepithelial Neoplasia Grading from Prepared Digital Histology Images

The paper proposes an integrated approach to the automated diagnosis of cervical intraepithelial neoplasia (CIN) in epithelial patches extracted from digital histology images. Experiments were conducted to determine the most suitable deep learning model for the dataset and fuse patch predictions to decide the final CIN grade of the histology samples. Seven candidate CNN architectures were assessed in this study. Three fusion methods were applied to the best CNN classifier. The model ensemble, combined CNN classifier and highest performing fusion method achieved an accuracy of 94.57%. This result shows significant improvement over the state-of-the-art classifiers for cervical cancer histopathology images. It is hoped that this work will contribute towards further research to automate diagnosis of CIN from digital histopathology images.