Diabetic Retinopathy (DR) is one of the most common eye diseases related to diabetics. If the diagnosis and treatment are conducted too late, it may result in various degrees of vision loss, even blindness. Therefore, individuals with diabetes should have a regular annual eye exam. Studies showed that early detection can prevent vision loss in earlier stages. However, in places such as undeveloped or developing countries, and even sometimes rural areas in developed countries, may not have enough resources for DR screening. Furthermore, even though these places may have adequate equipment, the diagnosis may take a few days to obtain results for the analysis of the ophthalmologists. Developing an automated detection algorithm is an emerging research area to diagnose DR remotely using a retina image. Localizing the optic disc and fovea is an essential task in these DR detection algorithms. After locating the optic disc, finding other components of the retina is easier. Technological developments in recent years enable the acceleration of diagnosis of such diseases including DR. Deep learning techniques are becoming an essential part of the medical field. In the last years, there have been many attempts to automatize the analysis of medical disorders such as breast cancer, glaucoma, diabetic macular edema, and diabetic retinopathy. In this paper, we presented the utilization of a pre-trained deep learning framework to localize the optic disc in the retina images. Using the transfer learning approach for AlexNet with a linear regression output, we localized the optic disc center. Retina images with labeled ground truth values of optic disc center were used to retrain the AlexNet. We tested our proposed deep learning-based optic disc localization approach with three different publicly available datasets including EyePACS, Messidor, and IDRID. Based on the results, the deep learning-based optic disc localization method shows high detection accuracy. The best results for optic disc detection were observed with cross dataset images as the accuracy of 88.35%, while a 97.66% testing accuracy was observed for the merged dataset using transfer learning approach for the pretrained AlexNet.
People with diabetes are at high risk of diabetic eye disease such as diabetic retinopathy (DR) which is the most common cause of vision loss. It is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since DR may cause no symptoms or only mild problems in its early stages, a diabetic person must have regular annual eye exams. During eye exams, doctors often image the retina using fundus cameras for diagnosis. However, fundus cameras are too large and heavy to be transported easily and too costly to be purchased by every health clinic. Therefore, there is a growing demand for small, portable, and inexpensive retinal imaging systems to perform fast DR screening. Recent technological developments have enabled the use of smartphones as biomedical imaging devices. The smartphonebased portable retinal imaging systems available on the market are used only to capture and save retinal images; they do not analyze them using any image processing techniques. In this paper, we investigated the smartphone-based portable ophthalmoscope systems available on the market and compared their field of view to determine if they are suitable for DR screening during a general health screening. Based on the results, iNview retinal imaging system has the largest field of view and better image quality compared with iExaminer, D-Eye, Peek Retina retinal imaging systems.