AI Driven Diagnosis Of Oral Malignancies Through Machine Learning and Deep Learning

Oral cancer presents a significant global health challenge, where early detection is critical for improving treatment outcomes. Differentiating between benign and malignant oral lesions typically requires expert analysis, often relying on specialized medical resources that are not universally accessible. This project aims to address this gap by developing an AI-driven diagnostic system designed to enhance the early detection and classification of oral lesions. Leveraging advanced image-based deep learning models, including MobileNet, Convolutional Neural Networks (CNN), and DenseNet, the system processes a dataset of oral lesion images to classify them accurately as benign or malignant. By analyzing subtle visual characteristics, the system offers a rapid, non-invasive diagnostic tool that aids healthcare providers in making timely, accurate decisions. The project's solution not only improves diagnostic accuracy but also addresses the pressing need for scalable, automated systems, particularly in regions with limited access to specialized healthcare resources. Ultimately, this system aims to support healthcare professionals, enhance early diagnosis, and contribute to better patient outcomes by enabling 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.