A Logical Design of Superficial Learning Methodology to Identify Dental Diseases Using Predictive Classification Principle

Dental disease detection is a critical aspect of oral healthcare, as early diagnosis can significantly improve treatment outcomes. In this study, we propose a novel Superficial Learning Methodology (SLM) for classifying dental diseases using image data from the “Oral Diseases-The Dental Condition Dataset,” which consists of 1,296 images representing six dental conditions: caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia. The proposed SLM integrates a cascaded ResNet for feature extraction and a Support Vector Machine (SVM) for classification. Several data preprocessing techniques, including image resizing, augmentation, normalization, CLAHE, and Gaussian filtering, were applied to enhance the model's performance. The SLM model was evaluated using accuracy, precision, recall, and F1-score, achieving an overall accuracy of 98.02%, surpassing traditional machine learning and deep learning models. The combination of ResNet's deep feature extraction capabilities with SVM's margin-based classification proved highly effective in classifying dental conditions with high precision and recall. Additionally, the model demonstrated competitive computational efficiency in terms of training time and complexity, making it suitable for practical clinical applications. This study highlights the potential of integrating deep learning and machine learning techniques for accurate dental disease classification.