Analysis of Dental X-Ray Images for the Diagnosis and Classification of Oral Conditions

Abstract

In medicine, deep convolutional neural networks are prevalent due to their effectiveness in detection, prediction, and classification. Without panoramic dental radiography, professionals are unable to detect anomalies at the back of the mouth, the buccal cavity, and elsewhere. Using panoramic X-rays, this study presents a novel method of automated tooth detection and dental disease categorization to aid physicians in making correct diagnosis. Precision, recall, F1-score, and accuracy were used to evaluate the approach's bounding box detections and semantic segmentation. Multiple techniques' data showed the superiority of recommended solutions.