NFR-EDL: Non-linear fuzzy rank-based ensemble deep learning for accurate diagnosis of oral and dental diseases using RGB color photography

Abstract

Background

Oral health plays a vital role in our daily lives, affecting essential activities like eating, speaking, and smiling. Poor oral health can lead to significant social, psychological, and physical consequences, which makes early and accurate diagnosis incredibly important. Recent advances in artificial intelligence (AI) are opening new doors in oral health care, offering faster, more accurate ways to identify dental issues and improve overall care.

Methods

This paper uses RGB color photography to introduce a non-linear Fuzzy Rank-based Ensemble Deep Learning model (NFR-EDL) for diagnosing oral and dental diseases. The model utilizes four deep Convolutional Neural Network (CNN) base models to analyze high-resolution color images of the oral cavity. The CNN base models are initially trained to generate confidence scores, which are subsequently mapped onto distinct functions with varying concavities, resulting in non-linear fuzzy ranks. Then, these ranks are combined into a final score to minimize the deviation from expected results. This method aims to provide accurate, reliable identification of oral and dental disease diagnosis by fusing many base models and considering uncertainty in decision-making while utilizing the rich visual information available in RGB images.

Results

The experimental results demonstrate that the proposed NFR-EDL model achieves accuracies of 97.08 %, 84.00 %, 89.86 %, and 94.66 % on the Kaggle, MOD, ODSI-DB, and OaDD datasets, respectively. These results demonstrate the model's exceptional accuracy and effectiveness in diagnosing oral and dental diseases, outperforming existing techniques and enhancing diagnostic reliability in clinical settings.

Conclusion

Deploying the NFR-EDL model in clinical settings offers a highly accurate and reliable tool for diagnosing oral and dental diseases, enhancing early detection, personalizing patient care, and reducing diagnostic errors to ultimately improve patient outcomes and the efficiency of dental care delivery. This approach reduces uncertainty in decision-making, ensuring that diagnoses are made with high confidence.