Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

Abstract

Oral cancer is the most commonly occurring ‘head and neck cancers’ across the globe. Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public. Since earlier identification of disease is essential for improved outcomes, Artificial Intelligence (AI) and Machine Learning (ML) models are used in this regard. In this background, the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model (AIDTL-OCCM). The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques. The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing. Followed by, the densely-connected networks (DenseNet-169) model is employed to produce a useful set of deep features. Moreover, Chimp Optimization Algorithm (COA) with Autoencoder (AE) model is applied for oral cancer detection and classification. Furthermore, COA is employed to determine optimal parameters involved in AE model. A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects. The extensive experimental analysis outcomes established the enhanced performance of AIDTL-OCCM model compared to other approaches with a maximum accuracy of 90.08%.