Tag: Fuzzy C-means
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A Computational Intelligence Approach for Classifying Dental Caries in X-ray Images
Introduction Detecting dental caries in radiographs is a foundational task in preventive dentistry. The low contrast and overlapping structures in bite-wing and periapical X-ray images can obscure early lesions, making accurate classification a persistent challenge. A novel computational intelligence framework combines integrated fuzzy C-means clustering with feature reduction and a weighted matrix scheme to address…
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A Computational Intelligence Pipeline for Dental Caries Classification in X-Rays
Introduction: The Need for Smart Caries Classification Decoding dental X-ray images to accurately identify caries remains a key challenge for clinicians. Low contrast, variable radiographic quality, and subtle lesion boundaries can hinder timely and precise diagnoses. A practical solution combines computational intelligence with robust feature engineering to improve detection and grading of carious lesions. This…
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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
Overview Dental caries detection in radiographs is a cornerstone of preventive dentistry. Traditional visual inspection can miss subtle demineralization, while raw image processing may struggle with low contrast and noise. This article outlines a computational intelligence framework that combines integrated fuzzy C-means clustering (FCM) with feature reduction and a weighted matrix scheme to classify dental…