The study proposes an integrated approach to automated cervical intraepithelial neoplasia (CIN) diagnosis in epithelial patches extracted from digital histology images. The model ensemble, combined CNN classifier, and highest-performing fusion approach achieved an accuracy of 94.57%. This result demonstrates significant improvement over the state-of-the-art classifiers for cervical cancer histopathology images and promises further improvement in the automated diagnosis of CIN.
Keywords: Automated diagnosis; cervical intraepithelial neoplasia (CIN); convolutional neural network (CNN); deep learning; fusion; histology image.