Dental Caries Diagnosis in X-ray Images Using KNN Classifier

Abstract

Objectives: Early diagnosis of dental caries helps in maintaining good oral health. The current study focuses on diagnosis of dental caries in dental radiographs through machine learning. Methods: Dental radiographic images in bmp format are considered for study. The images are trained, validated and tested with 10-fold cross validation. Diagnosis method involves Laplacian filter, adaptive thresholding, morphological transformation, Grey Level Co-occurrence Matrix (GLCM) based texture analysis and K Nearest Neighbors (KNN) Classifier. The diagnostic performance measures accuracy, False Positive Ratio (FPR), precision, recall, Mathews Correlation Coefficient (MCC), Receiver Operating Characteristic (ROC) area are calculated for detection and diagnosis of dental caries. Findings: Proposed method is giving better performance of 98.5% accuracy, 98.5% precision, 4.7% False Positive Rate (FPR) and 0.953 Receiver Operating Characteristic (ROC) curve area with 10-fold cross validation. The validity of the results tested using two-way ANOVA, at significant level of 5%, shows that the interaction of proposed method on performance parameter measures is significant. Applications/Improvement: The study highlighted the potential utility of machine learning for detection of dental caries in automated computer assisted diagnosis system. The proposed method provided good performance in detecting caries in dental radiographs. The results suggest that the proposed framework is a promising approach for the automatic detection of dental caries in dental radiographs. The performance of the system can be further improved by high quality and quantity dataset. Keywords: Computer Assisted Diagnosis, Dental Caries, Dental Radiography, Machine Learning