From Image to Diagnosis: Convolutional Neural Networks in Tongue Lesions

Abstract

Clinical examination of the tongue is essential for diagnosing systemic and local diseases. However, traditional diagnostic methods rely on subjective evaluation. Artificial intelligence, particularly convolutional neural networks, has shown promise in enhancing diagnostic accuracy in medical imaging. This study aimed to classify common tongue lesions using convolutional neural networks, improving diagnostic precision in routine dental examinations. A dataset of 1038 tongue images was analyzed, categorized into six classes: healthy, coated, fissured, hairy, geographic, and median rhomboid glossitis. The ResNet18 model was employed for binary classification, and ResNet50 for three-class classification. Preprocessing techniques, including image resizing and augmentation, were applied to optimize model performance. Performance was assessed using accuracy, precision, recall, and F1-score. The ResNet18 model achieved 100% accuracy in distinguishing healthy from hairy tongue lesions and demonstrated high performance in binary classification tasks. The ResNet50 model reached 96% accuracy for healthy-coated-hairy classification but faced challenges with other lesion groups. CNN-based models provide an effective, non-invasive tool for classifying tongue lesions, with ResNet18 excelling in binary classification. The findings suggest that artificial intelligence integration in maxillofacial radiology can enhance diagnostic reliability in routine dental practice. Further studies with larger datasets and real-time clinical applications are recommended to refine artificial intelligence-driven diagnostic tools.