Deep learning-based multiclass instance segmentation for dental lesion detection

2026-04-30T00:19:24Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/633922023-12-01T20:01:08Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Deep learning-based multiclass instance segmentation for dental lesion detection Fatima, Anum Shafi, Imran Afzal, Hammad Mahmood, Khawar Torre Díez, Isabel de la Lipari, Vivian Brito Ballester, Julien Ashraf, Imran Machine learning Aprendizaje automático Artificial intelligence - Medical applications Inteligencia artificial - Aplicaciones médicas Preventive dentistry Dental hygiene Higiene dental Mouth - Diseases - Diagnosis Boca - Enfermedades - Diagnóstico Image processing Imágenes, Tratamiento de las Health informatics Dentistry Odontología Producción Científica 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. 2023-12-01 2023-12-01 2023 info:eu-repo/semantics/article Healthcare, 2023, Vol. 11, Nº. 3, 347 2227-9032 https://uvadoc.uva.es/handle/10324/63392 10.3390/healthcare11030347 347 3 Healthcare 11 2227-9032 eng https://www.mdpi.com/2227-9032/11/3/347 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2023 The authors Atribución 4.0 Internacional MDPI