AI-Based Oral Cancer Detection Using Hybrid Deep Learning Models

The rising incidence and mortality rates of oral cancer have turned it into a significant global public health concern. Detecting the condition at an early stage is essential for achieving better health outcomes, as timely intervention significantly boosts the chances of effectively treating conditions such as Squamous Cell Carcinoma, Lymphoma, Melanoma, Kaposi’s Sarcoma, Osteosarcoma, and Adenoid Cystic Carcinoma. This proposal presents an AI-driven system designed to detect oral cancer through hybrid deep learning models. By integrating the EfficientNet and XceptionNet frameworks, the system autonomously extracts pertinent features from medical images and categorizes them as either cancerous or non-cancerous. The method utilizes a comprehensive dataset of oral cancer images, encompassing various lesion types, Gathered via clinical imaging techniques, including digital photography . The proposed system seeks to enhance the precision, dependability, and speed of oral cancer detection, providing a non-invasive tool to aid in early diagnosis and clinical decision-making.