Establishment of a deep learning-based visual model for intelligent recognition of Oncomelania hupensis
10.16250/j.32.1374.2021033
- VernacularTitle:基于深度学习技术的湖北钉螺 视觉智能识别模型的建立
- Author:
Liang SHI
1
;
Chun-Rong XIONG
1
;
Mao-Mao LIU
2
;
Xiu-Shen WEI
3
;
Xin-Yao WANG
1
;
Tao WANG
1
;
Yi-Xin HUANG
1
;
Qing-Biao HONG
1
;
Wei LI
1
;
Hai-Tao YANG
1
;
Jian-Feng ZHANG
1
;
Kun YANG
4
Author Information
1. Key Laboratory of National Health Commission on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Public Health Research Center of Jiangnan University, Wuxi 214064, China
2. School of Public Health, Nanjing Medical University, China
3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Jiangsu Provincial Key Laboratory of Image and Video Understanding for Social Safety, China
4. Key Laboratory of National Health Commission on Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Public Health Research Center of Jiangnan University, Wuxi 214064, China; School of Public Health, Nanjing Medical University, China
- Publication Type:Journal Article
- Keywords:
Oncomelania hupensis;
Deep learning;
Intelligent recognition;
Computer vision;
Machine learning;
Artificial intelligence
- From:
Chinese Journal of Schistosomiasis Control
2021;33(5):445-451
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a deep learning-based visual model for intelligent recognition of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, and evaluate the effects of different training strategies for O. hupensis image recognition. Methods A total of 2 614 datasets of O. hupensis snails and 4 similar snails were generated through field sampling and internet capture, and were divided into training sets and test sets. An intelligent recognition model was created based on deep learning, and was trained and tested. The precision, sensitivity, specificity, accuracy, F1 score and Youden index were calculated. In addition, the receiver operating characteristic (ROC) curve of the model for snail recognition was plotted to evaluate the effects of “new learning”, “transfer learning” and “transfer learning + data enhancement” training strategies on the accuracy of the model for snail recognition. Results Under the “transfer learning + data enhancement” strategy, the precision, sensitivity, specificity, accuracy, Youden index and F1 score of the model were 90.10%, 91.00%, 97.50%, 96.20%, 88.50% and 90.51% for snail recognition, which were all higher than those under both “new learning” and “transfer learning” strategies. There were significant differences in the sensitivity, specificity and accuracy of the model for snail recognition under “new learning”, “transfer learning” and “transfer learning + data enhancement” training strategies (all P values < 0.001). In addition, the area under the ROC curve of the model was highest (0.94) under the “transfer learning + dataenhancement” training strategy. Conclusions This is the first visual model for intelligent recognition of O. hupensis based on deep learning, which shows a high accuracy for snail image recognition. The “transfer learning + data enhancement” training strategy is helpful to improve the accuracy of the model for snail recognition.
