IDD-Net: A Deep Learning Approach for Early Detection of Dental Diseases Using X-Ray Imaging

Abstract

Early detection of dental diseases such as cavities, periodontitis, and periapical infections is crucial for effective management and prevention, as these conditions can lead to severe complications if left untreated. However, traditional diagnostic methods are often manual, time-consuming, and heavily reliant on expert judgment, which can introduce variability and delay in diagnosis. To address these critical challenges, we propose IDD-Net (Identification of Dental Disease Network), a novel deep learning-based model designed for the automatic detection of dental diseases using panoramic X-ray images. The proposed framework leverages Convolutional Neural Networks (CNN) to enhance the accuracy and efficiency of dental condition classification, thereby significantly improving the diagnostic process. In our comprehensive evaluation, IDD-Net’s performance is rigorously compared to four state-of-the-art deep learning models: AlexNet, InceptionResNet-V2, Xception, and MobileNet-V2. To tackle the issue of class imbalance, we employ the Synthetic Minority Over-sampling Technique with Tomek links (SMOTE Tomek), ensuring a balanced sample distribution that enhances model training. Experimental results showcase IDD-Net’s exceptional performance, achieving a 99.97% AUC, 98.99% accuracy, 98.24% recall, 98.99% precision, and a 98.97% F1-score, thus outperforming benchmark classifiers. These findings underscore the transformative potential of IDD-Net as a reliable and efficient tool for assisting dental and medical professionals in the early detection of dental diseases. By streamlining the diagnostic process, IDD-Net not only improves patient outcomes but also has the potential to reshape standard practices in dental care, paving the way for more proactive and preventive approaches in oral health management.