AI-Driven Diagnosis of Oral Malignancies through Machine Learning and Deep Learning

Abstract: Oral cancer is a serious global health issue. The success of treatment also largely depends on early diagnosis. The differentiation between benign and malignant oral lesions requires specialist expertise, which in itself is a challenge because of the lack of advanced medical facilities. This project hopes to address this gap through an AI-based diagnostic tool developed specifically for improving the early diagnosis and classification of oral lesions. It uses recent advancements in deep learning models like MobileNet, Convolutional Neural Networks, or DenseNet that make analyses of a dataset consisting of oral lesion images classify as benign or malignant. Focusing on subtle visual cues, this system serves as a rapid, non-invasive diagnostic tool that guides healthcare professionals toward making right and timely decisions. Thus, in addition to improving precision in diagnostics, it satisfies a critical demand for scalable automated solutions within areas that may lack access to specialized health care. Ultimately, the system would assist health care providers to provide an early diagnosis that contributes toward better patient outcomes due to timely intervention. Keywords: Oral cancer, AI-driven diagnostic system, image-based AI models, MobileNet, CNN, DenseNet, lesion classification, benign lesions, malignant lesions, deep learning, non-invasive diagnostics, machine learning.