TY  - JOUR
JF  - Healthcare
IS  - 3
UR  - http://doi.org/10.3390/healthcare11030347
A1  - Fatima, Anum
A1  - Shafi, Imran
A1  - Afzal, Hammad
A1  - Mahmood, Khawar
A1  - Díez, Isabel de la Torre
A1  - Lipari, Vivian
A1  - Brito Ballester, Julién
A1  - Ashraf, Imran
VL  - 11
ID  - unic5663
N2  - Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. 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, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a 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) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches
KW  - Mask-RCNN; MobileNet; deep learning; dental disease detection
AV  - public
Y1  - 2023///
SN  - 2227-9032
TI  - Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection
ER  -