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Table 1 Selected applications of deep learning

From: Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning

Disease type Diagnostic target Architecture Accuracy Control group
Skin cancer [9] Multiple classification Inception v3 CNN > 0.960 ~ 0.880
Congenital cataract [35] Binary classification,
object localization
VGG 0.976‒0.989
Pneumonia [36] Multiple classification, object detection CheXNet 0.735‒0.937 0.633‒0.914
Diabetic retinopathy [37] Binary classification Inception v3 0.840 0.800‒0.910
Cardiovascular disease [38] Binary classification 0.764 0.728
Alzheimer’s disease [40] Binary classification LeNet,
GoogLeNet
0.845‒0.988
Genetic disorders [46] Binary classification DeepGestalt 0.920‒0.969 0.710‒0.870
Cutaneous tumors[47 Multiple classification ResNet-152 0.96
Melanoma[48] Binary classification Inception v4 0.860 0.790
Malaria parasites [49, 50] Binary classification,
logistic regression
CNN 0.897
Cardiac contractile dysfunction [51] Binary classification CNN 0.835‒0.925
Arrhythmia [52] Multiple classification DNN 0.837 0.780
Melanoma and Seborrheic keratosis [53] Binary classification DCNN 0.917 0.886
Skin tumor [54] Multiple classification DCNN 0.924 0.853
Melanoma [55] Binary classification ResNet-152 0.944 0.823