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Table 1 Literature review of Melanoma classification using deep learning

From: Acral melanoma detection using dermoscopic images and convolutional neural networks

References Dataset Methods Classification Accuracy
Antony et al. [18] Self-collected ANN Melanoma 86.66%
Alquran et al. [6] ISIC SVM Melanoma 92.1%
Yang et al. [4] Self-collected Ridge and furrow pattern AM 99%
Esteva et al. [22] Self-collected Inception V3 Melanoma 72.1%
Yu et al. [10] Yonsei University VGG16 AM 80%
Hosny et al. [9] PH2 AlexNet Melanoma 98.33%
Praveenkumar and Dharmalingam [19] Self-collected ANN Melanoma 97%
Li et al. [23] ISIC CNNs Melanoma 82%
Salido and Ruiz [24] PH2 Deep CNN Melanoma 93%
Hosny et al. [15] ISIC, DERM AlexNet and augmentation Melanoma 87%
Sherif et al. [25] ISIC Deep CNN Melanoma 96.57%
Murugan et al. [8] ISIC SVM, kNN Melanoma 89.43%, 76.87%
Kassani SH and Kassani PH [20] ISIC Deep CNNs Melanoma 92%
Brinker et al. [21] ISIC CNNs Melanoma Specificity 86.5%,
sensitivity 74.1%