Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection

Abstract

Squamous cell carcinoma (SCC) of oral cavity is the most common among oral cancer patients. In this paper, we have developed machine learning based automatic oral squamous cell carcinoma (OSCC) classifier named as Stratified Squamous Epithelial Biopsy Image Classifier (SSE-BIC) to categorize H&E-stained microscopic images of squamous epithelial layer in four different classes: normal, well-differentiated, moderately-differentiated and poorly-differentiated. Five classifiers are used to perform the classification by maximum voting method. Total 305 features are extracted from the images of oral mucosa which include color features, textural features, gradient features, geometrical features and tamura features. Unsupervised data mining is used for segmenting the cellular area to compute geometrical features of the cells retaining color details of the images. Feature selection has been performed by neighborhood component feature selection (NCFS) technique. Total 676 images have been used to design, train and test the classifier. A detailed performance analysis is presented with individual feature sets and hybrid feature sets with feature selection applied using individual classifiers as well as proposed classifier. The proposed classifier achieves overall accuracy of 95.56%. This can account for first level of automatic screening of the biopsy images.