Oral Cancer Detection from Medical Images Using Machine Learning Techniques

Abstract

Oral cancer is a considerable worldwide health challenge, characterised by a high fatality rate mostly attributable to its late diagnosis. Traditional methods of diagnosing oral cancer, such as biopsies and visual examinations, often rely heavily on the expertise of specialists and can be time-consuming, subjective, and difficult to access, especially in resource-limited areas. This study investigates the potential of machine learning (ML), specifically Convolutional Neural Networks (CNNs), to automate the detection of oral cancer
through medical imaging. By analyzing various types of medical images, including histopathological slides, radiographs, and MRI scans, the research seeks to improve diagnostic precision, reduce human error, and facilitate prompt intervention. The proposed deep learning model shows promising results, achieving classification accuracy that surpasses traditional diagnostic methods. Experimental findings suggest that AI-powered image analysis can significantly improve early detection, support healthcare professionals in their decisionmaking, and ultimately enhance survival results for oral cancer patients.