Detecting Oral Cancer: A Machine Learning Approach Using Advanced Image Analysis Techniques

Abstract

Oral cancer remains a significant global health concern, with early diagnosis playing a critical role in enhancing treatment outcomes. Conventional diagnostic approaches, such as biopsies and visual examination, tend to be invasive and reliant on human judgment, which may result in diagnostic delays. This study investigates the use of a Convolutional Neural Network (CNN)-based model for the automated identification of oral cancer through the analysis of medical images, including histopathological samples and photographs of the oral mucosa. The CNN is trained using annotated image datasets, enabling it to distinguish between malignant and non-malignant tissues by learning hierarchical features through its convolutional architecture. To improve generalization and reduce overfitting, strategies like data augmentation, dropout layers, and regularization techniques are employed. The model’s effectiveness is measured using performance indicators such as accuracy, sensitivity, and specificity. The results demonstrate strong potential for this AI- driven method as a reliable, non-invasive solution for the early screening and detection of oral cancer.