Integrating Pretrained Deep Neural Networks with Traditional Classification Techniques for Enhanced Oral Cancer Diagnosis

The purpose of this research is to present a hybrid approach to the classification of oral cancer images. This approach combines traditional classification methods such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees with advanced feature extraction from pretrained deep neural networks (GoogleNet and MobileNetV2). Through the use of the suggested method, features are extracted from the deep learning models, resulting in the formation of a robust hybrid model that enhances diagnostic accuracy. The hybrid model achieves a classification accuracy of 90.01% with Quadratic SVM, which represents a 22.36% improvement over solo deep learning models. Comparative analyses indicate the tremendous performance advantages that the hybrid model has achieved. The findings highlight the potential of merging contemporary deep learning skills with older methods in order to improve the accuracy and dependability of medical picture categorization, particularly in the diagnostic process for oral cancer.