A computational intelligence approach for classifying dental caries in X-ray images using integrated fuzzy C-means clustering with feature reduction and a weighted matrix scheme.

Classifying dental caries in X-ray images poses several challenges that must be addressed to ensure accurate diagnosis and effective treatment planning. However, dental X-rays often have low contrast, making it difficult to distinguish between healthy and decayed regions, especially in the early stages of caries. This paper uses computational intelligence to present a new approach to classifying dental caries in X-ray images. The traditional Fuzzy C-means (FCM) segmentation algorithms have limitations, such as treating all feature components equally important. This can lead to misclassification in X-ray images due to the imbalance between caries-related and healthy features. To solve this problem, a computational intelligence approach is presented for the first time in dental X-ray analysis using the Feature Reduction and Weighted Scheme (FRWS). This method automatically calculates individual feature weights and reduces irrelevant features using a feature-reduction strategy and a feature-weighted scheme. By incorporating a feature-weight matrix, the algorithm can dynamically adjust the significance of dental caries and background features across different clusters and accurately determine the centers of smaller clusters in multidimensional feature spaces with imbalanced data. In addition to the algorithm, the MDOT (morphological dilation with optimal thresholding) technique is used to refine the classification of dental caries regions. The proposed method was evaluated on a dataset of 890 dental X-ray images, achieving an average accuracy of 91.62%, precision of 90.89%, specificity of 91.26%, sensitivity of 91.78%, a Dice coefficient of 90.74% and IoU of 83.90%. These results, particularly the high accuracy and precision, reassure the audience of the method's superior ability to classify caries lesions, instilling confidence in its performance. We demonstrate the feasibility of classifying dental caries in X-ray images using integrated fuzzy C-means clustering, feature reduction, and a weighted matrix scheme.