Simplified U-Net as a deep learning intelligent medical assistive tool in glaucoma detection

Glaucoma is looked on as the most important cause of irremediable vision loss worldwide. Early detection of eye diseases, especially glaucoma is serious for preparing timely medical care and keep downing the vision loss. In this paper, a fast segmentation algorithm is proposed which is based on a new simplified U-Net architecture for optic disc and retinal vessels segmentation. The proposed method includes a modified and reinforced structure that will reduce the prediction time while maintaining the performance and accuracy at an comparable level due to other state of the art methods. For example, for optic disc segmentation, the proposed method can segment the optic disc in 0.008 seconds on DRIONS-DB dataset, and for vessels segmentation,it can segment in 0.03 seconds on DRIVE dataset. According to these results and an extension of the proposed method can be used as a real-time intelligent medical system which able to be implemented on the usual hardware equipement in the ophthalmology clinics. This method, which can perform optic disc and retinal vessels segmentation tasks in a short time, increases the performance of ophthalmologists in glaucoma diagnosing.