Abstract：
BACKGROUND: Cryptococcosis and talaromycosis are known as \'neglected epidemics\' due to their high case fatality rates and low concern. Clinically, the skin lesions of the two fungal diseases are similar and easily misdiagnosed. Therefore, this study aims to develop an algorithm to identify cryptococcosis/talaromycosis skin lesions.
METHODS: Skin images of tararomiasis and cryptococcosis were collected from published articles and augmented using the Python Imaging Library (PIL). Then, five deep artificial intelligence models, VGG19, MobileNet, InceptionV3, Incept ResNetV2 and DenseNet201, were developed based on the collected datasets using transfer learning technology. Finally, the performance of the models was evaluated using sensitivity, specificity, F1 score, accuracy, AUC and ROC curve.
RESULTS: In total, 159 articles (79 for cryptococcosis and 80 for talaromycosis), including 101 cryptococcosis skin lesion images and 133 talaromycosis skin lesion images, were collected for further mode construction. Five methods showed good performance for prediction but did not yield satisfactory results for all cases. Among them, DenseNet201 performed best in the validation set, followed by InceptionV3. However, InceptionV3 showed the highest sensitivity, accuracy, F1 score and AUC values in the training set, followed by DenseNet201. The specificity of DenseNet201 in the training set is better than that of InceptionV3.
CONCLUSIONS: DenseNet201 and InceptionV3 are equivalent to the optimal model in these conditions and can be used in clinical settings as decision support tools for the identification and classification of skin lesions of cryptococcus/talaromycosis.
METHODS: Skin images of tararomiasis and cryptococcosis were collected from published articles and augmented using the Python Imaging Library (PIL). Then, five deep artificial intelligence models, VGG19, MobileNet, InceptionV3, Incept ResNetV2 and DenseNet201, were developed based on the collected datasets using transfer learning technology. Finally, the performance of the models was evaluated using sensitivity, specificity, F1 score, accuracy, AUC and ROC curve.
RESULTS: In total, 159 articles (79 for cryptococcosis and 80 for talaromycosis), including 101 cryptococcosis skin lesion images and 133 talaromycosis skin lesion images, were collected for further mode construction. Five methods showed good performance for prediction but did not yield satisfactory results for all cases. Among them, DenseNet201 performed best in the validation set, followed by InceptionV3. However, InceptionV3 showed the highest sensitivity, accuracy, F1 score and AUC values in the training set, followed by DenseNet201. The specificity of DenseNet201 in the training set is better than that of InceptionV3.
CONCLUSIONS: DenseNet201 and InceptionV3 are equivalent to the optimal model in these conditions and can be used in clinical settings as decision support tools for the identification and classification of skin lesions of cryptococcus/talaromycosis.
摘要：
背景：隐球菌病和塔拉真菌病由于其高病死率和低关注度而被称为“被忽视的流行病”。临床上,两种真菌病的皮肤病变相似，容易误诊。因此,本研究旨在开发一种算法来识别隐球菌病/塔拉真菌病皮肤病变。
方法：从已发表的文章中收集和使用Python成像库（PIL）增强的tarromiasis和隐球菌病的皮肤图像。然后，五种深度人工智能模型，VGG19,MobileNet,InceptionV3，InceptResNetV2和DenseNet201是基于使用迁移学习技术收集的数据集开发的。最后,使用灵敏度评估模型的性能，特异性，F1得分,准确度,AUC和ROC曲线。
结果：总计，159篇(隐球菌病79篇，塔拉真菌病80篇)，包括101例隐球菌病皮肤病变图像和133例塔拉真菌病皮肤病变图像，被收集用于进一步的模式构建。五种方法显示出良好的预测性能，但在所有情况下都没有产生令人满意的结果。其中,DenseNet201在验证集中表现最好，其次是InceptionV3。然而,InceptionV3显示出最高的灵敏度，准确度,训练集中的F1得分和AUC值，其次是DenseNet201。训练集中DenseNet201的特异性优于InceptionV3。
结论：DenseNet201和InceptionV3相当于这些条件下的最佳模型，可在临床环境中用作识别和分类隐球菌/牛痘皮损的决策支持工具。
方法：从已发表的文章中收集和使用Python成像库（PIL）增强的tarromiasis和隐球菌病的皮肤图像。然后，五种深度人工智能模型，VGG19,MobileNet,InceptionV3，InceptResNetV2和DenseNet201是基于使用迁移学习技术收集的数据集开发的。最后,使用灵敏度评估模型的性能，特异性，F1得分,准确度,AUC和ROC曲线。
结果：总计，159篇(隐球菌病79篇，塔拉真菌病80篇)，包括101例隐球菌病皮肤病变图像和133例塔拉真菌病皮肤病变图像，被收集用于进一步的模式构建。五种方法显示出良好的预测性能，但在所有情况下都没有产生令人满意的结果。其中,DenseNet201在验证集中表现最好，其次是InceptionV3。然而,InceptionV3显示出最高的灵敏度，准确度,训练集中的F1得分和AUC值，其次是DenseNet201。训练集中DenseNet201的特异性优于InceptionV3。
结论：DenseNet201和InceptionV3相当于这些条件下的最佳模型，可在临床环境中用作识别和分类隐球菌/牛痘皮损的决策支持工具。
