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