Diagnosis of common eye diseases in retinal fundus images containing cataract using deep learning network

One of the ways to diagnose eye diseases is to examine the Retinal Fundus (RF) images by a specialist. But in patients with cataract disease, it is very difficult to diagnose other diseases due to the blurring of the RF images. The purpose of this article is to present a method based on deep learning to increase the accuracy of diagnosing common eye diseases in the presence of cataracts and other retinal lesions. In the proposed method, in order to solve the problem of mutual overlap between eye diseases, which leads to incorrect diagnosis of the disease, the weighting technique is used in the training of the proposed model in order to increase the detection capability of the deep learning network. Also, due to the limitation in the number of images containing cataracts, including other eye diseases for training the deep learning network, various models of destruction of RF images are used to artificially simulate the images of cataracts and some retinal lesions. The results of the evaluation on the reference databases of the RF images suffering of cataract show that the proposed algorithm has been able to achieve diagnosis accuracy value of 80, 82, 79, 81, 80 and 65, respectively, for Age-Related Macular Degeneration (ARMD), Myopia (MYA), Tessellation (TSLN), Glaucoma (GL), Neovascularization (NE), and Diabetic Retinopathy (DR).