Improving the Performance of Convolutional Neural Network for the Segmentation of Optic Disc in Fundus Images Using Attention Gates and Conditional Random Fields

The localization and segmentation of optic disc (OD) in fundus images is a crucial step in the pipeline for detecting the early onset of retinal diseases, such as macular degeneration, diabetic retinopathy, glaucoma, etc. In this paper, we are proposing a novel convolutional neural network architecture for the precise segmentation of the OD in fundus images. We modify the basic architectures of DeepLab v3+ and U-Net models by integrating a novel attention module between the encoder and decoder to attain the finest accuracy. We also use fully-connected conditional random fields to further boost the performance of these architectures. We compare the results of our best proposed architecture against other established architectures for optic disc segmentation on our private dataset, as well as on publicly available datasets, namely, DRIONS-DB, RIM-ONE v.3, and DRISHTI-GS. The results obtained with the proposed method outperforms the existing methods in the literature.

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