An Overview of Infra-red Image Processing Based Oral Cancer Detection Technique

Abstract

Oral cancer, a prevalent global health concern often diagnosed at advanced stages, demands early and accurate detection for improved patient outcomes and reduced healthcare costs. This paper investigates the utility of Infrared Imaging (IR) for early detection of oral cancer, emphasizing its capacity to capture temperature variations associated with pathological changes. Addressing challenges in acquiring IR images from the oral cavity, the study highlights the necessity for precise diagnostic tools. This system uses thermal imaging and machine learning to extract and analyze the temperature data for predictions. With promising results from a rigorous evaluation of 24 subjects, the system demonstrates a sensitivity of 66.67% and specificity of 66.67%, indicating room for improvement. The study underscores the importance of collaborative research and algorithm refinement, utilizing SVM classifiers and Fuzzy logic to enhance the system's diagnostic accuracy and impact on healthcare outcomes. The findings emphasize the critical role of IR technology in revolutionizing oral cancer screening systems, highlighting the need for continued research and collaboration to optimize its practical application.