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Skin Image Analysis for Erythema Migrans Detection and Automated Lyme Disease Referral

机译:皮肤图像分析用于红斑偏头痛检测和自动莱姆病转诊

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摘要

This study develops approaches for the automated referral of individuals with Lyme disease using erythema migrans rash (EM) images with clinical-grade or 'in the wild' characteristics. We develop a pre-screener using a Deep Convolutional Neural Network (DCNN) that classifies EM vs. other conditions, including either control/unaffected skin, or skin presenting with other confuser lesions. We test and report performance metrics for the proposed approach on this dataset including Cohen's Kappa coefficient, area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity. The machine classification yields accuracy (and error margin) of 93.04% (1.49), AUC of 0.9504 (0.0156), and Kappa of 0.7549 (0.0586), which is a significant improvement over previously published state-of-the-art methods. Results also suggest substantial agreement between machine and expert clinician annotated gold standard images. The DCNN model developed for this skin classifier is made publicly available and can potentially be used by others for transfer learning to other types of skin lesion classification models including those for skin cancer.
机译:这项研究开发了使用具有临床级别或“在野外”特征的红斑皮疹(EM)图像自动转诊莱姆病患者的方法。我们使用深度卷积神经网络(DCNN)开发了一种预筛查器,该筛查器将EM与其他情况(包括对照/未受影响的皮肤或存在其他混淆性病变的皮肤)进行分类。我们在此数据集上测试并报告了所建议方法的性能指标,包括Cohen的Kappa系数,接收器工作特性(ROC)曲线(AUC)下的面积,准确性,灵敏度,特异性。机器分类可产生93.04%(1.49),AUC为0.9504(0.0156)和Kappa为0.7549(0.0586)的准确度(和误差容限),这是对以前发布的最新方法的重大改进。结果还表明,机器和专家临床医生对金标准图像进行了注释。为该皮肤分类器开发的DCNN模型可公开获得,并且可能被其他人用于将学习转移到其他类型的皮肤病变分类模型,包括皮肤癌分类模型。

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