A Multimodal Detection for Dental Disease Classification

Author(s)	Ms. R. Jenifer, Prof. J. Princess Bala
Country	India
Abstract	This project proposes an AI-assisted dental disease classification system using the dataset. The system classifies dental X-ray images into four categories: Caries, Crown, Filling, and Periapical Lesion. Three deep learning models, ResNet50, MaxViT, and Swin V2 Tiny, are implemented and compared for automated disease classification. Image preprocessing and augmentation are applied to improve model performance. The models are evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The system also predicts the disease class and confidence score for a given X-ray image. The proposed approach aims to support efficient and consistent preliminary dental disease identification through computer-aided analysis of dental X-ray images.
Keywords	dental x-ray images, deep learning, ResNet50, MaxViT, Swin V2 Tiny, image classification, transfer learning, computer-aided diagnosis, medical image analysis.
Field	Engineering
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-10-08
DOI	https://doi.org/10.36948/ijfmr.2026.v08i05.88904

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