关键词: 湖北钉螺, 智能识别, 深度学习, 计算机视觉, 机器学习, 血吸虫病, 监测

Abstract:

Objective To investigate the performance of a deep learning-based visual intelligent recognition model for the intermediate host of Schistosoma japonicum, the snail Oncomelania hupensis, and evaluate its efficacy in recognition and classification. Methods According to the distribution pattern of O. hupensis in the topographic type of lake marsh, hill and water network in Jiangsu Province, seven regions including Nanjing, Zhenjiang, Yangzhou, Suzhou, Changzhou, Wuxi and Yancheng were selected as the fields for sample collection from March 2019 to October 2020. From each of the fields, 3 snail breeding environment sites were randomly selected for collection of snail samples and images by smart phone. The image quality and morphologic features of the sampled snails were screened and classified by six experts of schistosomiasis control to establish a standard data set of snail image classification. The data set was compiled into training set and test set (comprised of internal and external test). Three major convolutional neural network models including MobilenetV2, ResNet50 and Inception-ResNet-V2 were used to exercise the model training in the training data set. Taking the standard snail image classification data set as the gold standard, the receiver operating characteristic (ROC) curves was applied to compare the sensitivity, specificity, diagnostic concordance rate (Kappa value), and Yorden index of three model types with the internal test data; comparison was performed between the best-fit recognition model and the findings of snail search staff through the external test set, evaluating the accuracy of models in snail recognition with the internal and external test set. Results Totally, 3 224 images of 4 types of snails similar to O. hupensis were collected: Semisulcospira cancellata, Opeas gracile, Euphaedus, and Tricula. After screening, 2 719 images were included into the standard snail image classification data set, among them 774 were of O. hupensis and 1 945 of 4 being similar to but not O. hupensis. The concordance Kappa value from the snail recognition models of MobilenetV2, ResNet50 and Inception-ResNet-V2 model with the gold standard was 0.78, 0.83 and 0.88 respectively. The sensitivity, specificity, accuracy, Yorden index, and area under the ROC curve (AUC) of the Inception-ResNet-V2 were found highest, being 92.00%, 97.16%, 96.13%, 0.89 and 0.95, respectively. There was no statistically significant difference in the sensitivity and specificity among the three models (χ² = 3.892, 4.948, P > 0.05), while the difference in accuracy was statistically significant (χ² = 8.607, P < 0.05). In the external test, the best-fit model Inception-ResNet-V2 and the findings by staff showed better concordance with the gold standard, having the Kappa values of 0.80 and 0.83, and the AUCs 0.88 and 0.92, respectively, of which the difference was not statistically significant (P > 0.05). Conclusion The deep learning-based visual intelligent recognition model for O. hupensis snailshowed evident accuracy.

Key words: Oncomelania hupensis, Intelligent recognition, Deep learning, Computer vision, Machine learning, Schistosomiasis, Surveillance