A multi-fusion approach to classify pharyngitis, tonsillitis and oral cancer with iterative relief feature weighting

Abstract

Bacterial pharyngitis and tonsillitis can lead to severe complications if untreated, while oral cancer poses risks of spreading if not detected early. Classifying pharyngitis, oral cancer, and tonsillitis is essential for timely and accurate medical interventions. Early diagnosis enables effective treatment planning, potentially saving lives and minimizing complications. Classification aids in tailoring specific medical approaches, contributing to better patient outcomes and healthcare management. The global COVID-19 pandemic has prompted a renewed interest in telemedicine for managing these conditions. Existing techniques, like Bag of Visual Words and individual pre-trained models, fall short in achieving optimal classification. To address this, we propose GHBResIncep, a framework that combines deep features from SE-ResNet, Inception-v4 and EfficientNetV2 with shallow features from improved BOVW, HOG, and Gabor filters. Employing an iterative relief feature weighting algorithm enhances feature selection. Our proposed model achieved highest classification accuracy of 95.58 %.The multi-fused features are fed into ML classifiers like XGBoost, showcasing significant improvements in various classification parameters compared to prior works.