Oral Disease Detection Using Deep Learning

Developed an AI-based system capable of detecting and classifying seven common oral diseases using deep learning and computer vision techniques. The model utilizes MobileNetV2, a lightweight and efficient CNN architecture, to analyze oral cavity images and identify visual symptoms such as discoloration, ulcers, or abnormal patches. This project aims to assist dentists and healthcare professionals in early diagnosis and automated screening of oral diseases through image-based analysis. The dataset includes thousands of annotated oral images categorized into seven classes. Detected Diseases: Oral Cancer Mucosal Conditions Gum Disease (Periodontal Disease) Candidiasis Cold Sores (Herpes Simplex) Oral Lichen Planus Oral Thrush Key Features: Multi-class classification of oral diseases High model accuracy and efficient inference time Image preprocessing and data augmentation applied User-friendly interface for clinical testing