Machine Learning Applications in Oral Cancer Detection and Diagnosis

Abstract

This paper explores the applications of machine learning (ML) in the field of oral cancer detection and diagnosis. Oral cancer is a significant public health issue, and early detection is crucial for successful treatment outcomes. ML algorithms have shown promise in improving the accuracy and efficiency of oral cancer diagnosis. This study reviews various ML techniques used in oral cancer detection, such as support vector machines, neural networks, and deep learning. It also discusses the challenges and future directions of ML in this domain. The findings suggest that ML has the potential to enhance oral cancer diagnosis, leading to improved patient outcomes.