  In this paper, we proposed Transferable Ranking Convolutional Neural Network
(TRk-CNN) that can be effectively applied when the classes of images to be
classified show a high correlation with each other. The multi-class
classification method based on the softmax function, which is generally used,
is not effective in this case because the inter-class relationship is ignored.
Although there is a Ranking-CNN that takes into account the ordinal classes, it
cannot reflect the inter-class relationship to the final prediction. TRk-CNN,
on the other hand, combines the weights of the primitive classification model
to reflect the inter-class information to the final classification phase. We
evaluated TRk-CNN in glaucoma image dataset that was labeled into three
classes: normal, glaucoma suspect, and glaucoma eyes. Based on the literature
we surveyed, this study is the first to classify three status of glaucoma
fundus image dataset into three different classes. We compared the evaluation
results of TRk-CNN with Ranking-CNN (Rk-CNN) and multi-class CNN (MC-CNN) using
the DenseNet as the backbone CNN model. As a result, TRk-CNN achieved an
average accuracy of 92.96%, specificity of 93.33%, sensitivity for glaucoma
suspect of 95.12% and sensitivity for glaucoma of 93.98%. Based on average
accuracy, TRk-CNN is 8.04% and 9.54% higher than Rk-CNN and MC-CNN and
surprisingly 26.83% higher for sensitivity for suspicious than multi-class CNN.
Our TRk-CNN is expected to be effectively applied to the medical image
classification problem where the disease state is continuous and increases in
the positive class direction.
