@article{unic5663,
           pages = {347},
         journal = {Healthcare},
          number = {3},
           title = {Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection},
          author = {Anum Fatima and Imran Shafi and Hammad Afzal and Khawar Mahmood and Isabel de la Torre D{\'i}ez and Vivian Lipari and Juli{\'e}n Brito Ballester and Imran Ashraf},
          volume = {11},
            year = {2023},
        abstract = {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},
        keywords = {Mask-RCNN; MobileNet; deep learning; dental disease detection},
             url = {http://repositorio.unic.co.ao/id/eprint/5663/}
}