Detect Glaucoma with Image Segmentation and Transfer Learning

In this paper, we aim to automatically detect glaucoma via deep learning. To do that, we need to calculate the cup-to-disc ratio (CDR) on fine segmented retina images. To get precise segmentation, we implemented SegNet together with adversarial discriminative domain adaptation (ADDA), the former is a famous artificial neural network with encoder-decoder architecture used in image segmentation area and the latter is a transfer learning method for domain adaptation. We are the first to combine them together to detect glaucoma on test dataset which have different brightness from our training dataset. We thoroughly evaluated the proposed method with various loss functions, normal cross entropy loss, weighted cross entropy loss and dice coefficient loss included. And we show that dice loss is the best for this task. Last but not least, our experiments on transfer learning have shown that our ADDA method reduces the mean square error (MSE) between the CDR of our segmentation and annotations greatly.