Deep Learning Algorithms in Oral Lesion Diagnosis: Innovations in Image-Based optimization for Cancer Detection and Differential Diagnosis

A part from being a challenge routine clinical practice, clinical oral lesions can begin from benign pathology to malignant diseases like OSCC (Oral Squamous Cell Carcinoma.). This preliminarily exploratory research aims at evaluating the applicability of the DL algorithms for the unobtrusive interpretation of clinical oral photographs in relation to early identification of cancer and accurate distinction of various types of OM lesions. The lesion data were gathered from the hospitals and authentic online sources, and meaningful CNNs and other frameworks like Res-Net and YOLO algorism were used for classifying the lesions with higher test accuracy. The models’ performance was high with task performance AUCs above 0.95, which helped to distinguish between malignant and benign lesions. The heatmap-based interpretability helped in workings out particular diagnostic features that needed validation from clinicians. This project shows that more DL solutions could be applied to oral diagnostics, which would lead to the development of economical, precise, and non-erosive technologies to improve early detection, sorting, and differential diagnosis of oral diseases. The future work consists in enriching data samples and improving multi-class classification.