Automated Oral Diseases Detection Using Deep Learning and Image Processing

Abstract

The early-stage oral disease detection system is designed to improve oral healthcare diagnostics through the use of advanced deep learning techniques for accurate, efficient, and real-time disease identification. Oral diseases such as oral cancer, leukoplakia, gingivitis, periodontitis, ulcers, fungal infections, and other precancerous lesions are common worldwide, making early diagnosis essential for preventing serious complications and improving patient outcomes. Conventional diagnostic methods mainly rely on manual clinical examination, biopsy procedures, and visual inspection, which may be time-consuming, subjective, and sometimes prone to delayed detection. To address these challenges, the proposed system adopts a multimodal deep learning framework that combines oral image analysis with clinical text data for comprehensive diagnosis. MobileNet, a lightweight Convolutional Neural Network (CNN), is used to process dental X-ray and intraoral images to identify important visual features such as cavities, lesions, gum abnormalities, and tissue changes Simultaneously, Word2V embeddings integrated with BiLSTM are applied to analyze patient records, symptoms, medical history, and clinical notes by capturing contextual textual information. The extracted image and text features are fused into a unified multimodal representation, enabling more precise and intelligent disease prediction. This integrated approach improves diagnostic accuracy, reduces false positives, enhances clinical decision support, and provides faster evaluations. Ultimately, the system supports dental professionals with reliable AI-driven tools, promoting precision, accessibility, and innovation in modern oral healthcare.