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Fig. 5 | BioMedical Engineering OnLine

Fig. 5

From: Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Fig. 5

CS-ResCNN feature maps and representative conventional features using t-SNE. af Two-dimensional maps of LBP, SIFT, WT, COTE, ResCNN and CS-ResCNN methods, respectively. The red and green dots represent positive and negative samples. t-SNE, t-distributed stochastic neighbor embedding; CS-ResCNN, cost-sensitive residual convolutional neural network; WT, wavelet transformation; SIFT, scale-invariant feature transform; LBP, local binary pattern; COTE, color and texture features

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