Neural networks in analyzing fuzzy information of OCT

In this regard, in the framework of deep learning and based on inaccurate information obtained from images of retinal layers, an end-to-end network has been trained to analyze and segment retinal layers. The result of this study is the introduction of a kit that can be installed on smartphones, which can be used to easily and simply monitor users' vision.The input of this structure is OCT images of the eye, without the need to extract hand-crafted features as input from the image, and its output is accurate spatial information of the retinal layers. The time required for each scan of OCT input images in this kit is about 10ms, which makes it possible to use such kits in a variety of smartphones easily. This kit, which assesses the health of ocular arteries, is inexpensive and can be used for remote locations or people who are not able to visit specialized eye centers or need frequent and continuous monitoring of their eye health. The ranking results presented in the present article show that the proposed structure has an acceptable performance and is very close to the average opinions of the group of specialist ophthalmologists. The results of evaluations and comparisons are made within the framework of multi-criteria decision-making approach. In order to extract the results in this framework, the TOPSIS algorithm is used in which the weights of the indicators are determined based on the entropy size.