|
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.
|
