Hybrid Feature Extraction Based Ensemble Classification Model to Diagnose Oral Carcinoma Using Histopathological Images

Abstract

Detection and classification of cancerous tissue from histopathologic images is quite a challenging task for pathologists and computer assisted medical diagnosis systems because of the complexity of the histopathology image. For a good diagnostic system, feature extraction from the medical images plays a crucial role for better classification of images. Using inappropriate or redundant features leads to poor classification results because classification algorithm learns a lot of unimportant information from the images. We propose hybrid feature extractor using different feature extraction algorithms that can extract various types of features from histopathological image. For this study, feature fused Convolution Neural Network, Gray Level Cooccurrence Matrix, and Local Binary Pattern algorithms are used. The texture and deep features obtained from these methods are used as input vector to classifiers: Support Vector Machine, KNearest Neighbor, Naïve Bayes and Boosted Tree. Prediction results of these classifiers are combined using soft majority voting algorithm to predict final output. Proposed method achieved an accuracy of 98.71%, which is quite high as compared to previous similar research works. Proposed method was capable of identifying most of cancerous histopathology images. The combination of deep and textural features can be potentially used for creating computer assisted medical imaging diagnosis system that can detect cancer from histopathology images timely and accurately.