2026-04-26T21:57:37Zhttps://uvadoc.uva.es/oai/request

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

Fatima, Anum Shafi, Imran Afzal, Hammad Mahmood, Khawar Torre Díez, Isabel de la Lipari, Vivian Brito Ballester, Julien Ashraf, Imran 2023-12-01T09:07:59Z 2023-12-01T09:07:59Z 2023 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 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. application/pdf eng MDPI info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2023 The authors Atribución 4.0 Internacional 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 1203.04 Inteligencia Artificial 1203.17 Informática 3213.13 Ortodoncia-Estomatología Deep learning-based multiclass instance segmentation for dental lesion detection info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/2227-9032/11/3/347 SI