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.
