An Automated Dental Lesion Detection System Based on Deep Learning

Abstract

Artificial intelligence (AI) has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, machine and deep learning models for dental disease diagnoses using x-ray imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. In this regard, lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region based network (RPN) is proposed for periapical disease localization on small dataset. To measure the effectiveness of the proposed model, lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results indicate that the proposed model was able to detect and localize periapical lesions with an overall accuracy of 94%, mean average precision (mAP) of 85% and mIoU of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using smaller number of images compared to existing methods.