MobileNetV3 network-based diagnosis of caries and periapical periodontitis from periapical films
10.12016/j.issn.2096-1456.2024.01.007
- Author:
WANG Kaixin
1
;
LIU Feng
1
;
ZENG Lingfang
2
;
LIU Chao
3
Author Information
1. School of Information Science and Technology, Shandong University
2. Pediatric Dentistry Department 1, Jinan Stomatological Hospital
3. Department of Oral and Maxillofacial Surgery, Qilu Hospital of Shandong University
- Publication Type:Journal Article
- Keywords:
dental disease / caries / periapical periodontitis / periapical film / intelligent diagnosis / image processing / deep learning / MobileNetV3 network / class activation map / visualization analysis
- From:
Journal of Prevention and Treatment for Stomatological Diseases
2024;32(1):43-49
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To research the effectiveness of deep learning techniques in intelligently diagnosing dental caries and periapical periodontitis and to explore the preliminary application value of deep learning in the diagnosis of oral diseases
Methods:A dataset containing 2 298 periapical films, including healthy teeth, dental caries, and periapical periodontitis, was used for the study. The dataset was randomly divided into 1 573 training images, 233 validation images, and 492 test images. By comparing various neural network models, the MobileNetV3 network model with better performance was selected for dental disease diagnosis, and the model was optimized by tuning the network hyperparameters. The accuracy, precision, recall, and F1 score were used to evaluate the model's ability to recognize dental caries and periapical periodontitis. Class activation map was used to visualization analyze the performance of the network model
Results:The algorithm achieved a relatively ideal intelligent diagnostic effect with precision, recall, and accuracy of 99.42%, 99.73%, and 99.60%, respectively, and the F1 score was 99.57% for classifying healthy teeth, dental caries, and periapical periodontitis. The visualization of the class activation maps also showed that the network model can accurately extract features of dental diseases.
Conclusion:The tooth lesion detection algorithm based on the MobileNetV3 network model can eliminate interference from image quality and human factors and has high diagnostic accuracy, which can meet the needs of dental medicine teaching and clinical applications.
- Full text:基于MobileNetV3网络的龋病和根尖周炎根尖片的诊断.pdf
