基于迁移学习和数据增强策略构建真菌性角膜炎镰刀菌属鉴定的智能诊断系统

Diagnosis model for fusarium identification of fungal keratitis based on transfer learning and data augmentation

摘要:

目的：构建和评估一种基于迁移学习和数据增强策略的真菌性角膜炎镰刀菌属鉴定的智能诊断模型。

方法：回顾性分析。纳入2017-03/2020-01在广西壮族自治区人民医院眼科行活体共聚焦显微镜检查的真菌性角膜炎患者的2 157张图像构建数据集，并根据微生物培养结果对数据集进行分类，将数据集划分为训练集1 380张、验证集345张和测试集432张。采用迁移学习Inception-ResNet V2网络构建智能诊断模型，将原始数据集和应用数据增强策略后的增强数据集所训练的智能诊断系统进行对比，最后计算智能诊断系统的特异度、灵敏度、准确率和受试者工作特征曲线的曲线下面积(AUC)等指标，评估该系统的诊断效能。

结果：使用原始数据集训练的智能诊断系统的特异度为71.6%，灵敏度为72.0%，准确率为71.8%，AUC为0.785(95%CI：0.742～0.828，P<0.0001)； 使用增强数据集训练的智能诊断系统的特异度为76.6%，灵敏度为83.1%，准确率为79.9%，AUC为0.876(95%CI：0.843～0.909，P<0.0001)，使该智能诊断系统的诊断效能均较前提高。

结论：通过迁移学习的方式构建出真菌性角膜炎镰刀菌属的智能诊断系统，具有较高的准确性，实现了对真菌性角膜炎病原菌镰刀菌属的智能诊断，并进一步验证在原始数据集有限的情况下，采用数据增强策略可以提高系统的诊断性能，该方法可用于真菌性角膜炎病原学镰刀菌属鉴定的辅助诊断。

Abstract:

AIM: To construct and evaluate a diagnostic model based on transfer learning and data augmentation as a non-invasive tool for fusarium identification of fungal keratitis.

METHODS: A retrospective study. In this study, 2 157 images of fungal keratitis patients who underwent in vivo confocal microscopy examination in the Department of Ophthalmology of the people's Hospital of Guangxi Zhuang Autonomous Region from March 2017 to January 2020 were included as the dataset, which was classified according to the results of microbial culture. The dataset was subsequently randomly divided into training set(1 380 images), validation set(345 images)and test set(432 images). We used the transfer learning Inception-ResNet V2 network to construct a diagnostic model, and to compare the performance of the model trained on different datasets. The performance of the diagnostic model evaluated with specificity, sensitivity, accuracy, and area under the receiver operating characteristics curve(AUC).

RESULTS: The model trained with the original training set had a specificity rate of 71.6%, a sensitivity rate of 72.0%, an accuracy rate of 71.8% and AUC of 0.785(95%CI: 0.742-0.828, P<0.0001). And the model trained with the augmented training set had a specificity rate of 76.6%, a sensitivity rate of 83.1%, an accuracy rate of 79.9% and AUC of 0.876(95%CI: 0.843-0.909, P<0.0001), which made the model's prediction performance boost.

CONCLUSION: In this study, we constructed an intelligent diagnosis system for fungal keratitis fusarium through transfer learning, which has higher accuracy, and realized the intelligent diagnosis of fungal keratitis pathogen fusarium. Furthermore, we verified that the data augmentation strategy can improve the performance of the intelligent diagnosis system when the original dataset is limited, and this method can be used for intelligent diagnosis and identification of fungal keratitis pathogen fusarium.