شبکه های عصبی در تحلیل اطلاعات فازی از تصاویر شبکیه چشم

در اﯾﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﺗﺤﻠﯿﻞ اﻃﻼﻋﺎت ﻧﺎدﻗﯿﻖ از ﺗﺼﺎوﯾﺮ ﺷﺒﮑﯿﻪ ﭼﺸﻢ اﻧﺴﺎن در ﻗﺎﻟﺐ ﯾﮏ ﮐﯿﺖ ﻗﺎﺑﻞ ﻧﺼﺐ ﺑﺮ ﮔﻮﺷﯽﻫﺎی ﻫﻮﺷﻤﻨﺪ ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ. در اﯾﻦﺧﺼﻮص اﯾﻦ ﻣﻨﻈﻮر در ﭼﺎرﭼﻮب ﯾﺎدﮔﯿﺮی ﻋﻤﯿﻖ و ﺑﺮ اﺳﺎس اﻃﻼﻋﺎت ﻧﺎدﻗﯿﻖ ﺣﺎﺻﻞ از ﺗﺼﺎوﯾﺮ ﻻﯾﻪﻫﺎی ﺷﺒﮑﯿﻪ ﺑﻪ آﻣﻮزش ﯾﮏ ﺷﺒﮑﻪ اﻧﺘﻬﺎ-ﺑﻪ-اﻧﺘﻬﺎ ﺑﻪ ﻣﻨﻈﻮر ﺗﺤﻠﯿﻞ و ﻗﻄﻌﻪﺑﻨﺪی ﻻﯾﻪﻫﺎی ﺷﺒﮑﯿﻪ ﻣﺒﺎدرت ﺷﺪه اﺳﺖ. ﻧﺘﯿﺠﮥ اﯾﻦ ﻣﻄﺎﻟﻌﻪ ﺑﻪ ﻣﻌﺮﻓﯽ ﯾﮏ ﮐﯿﺖ ﻗﺎﺑﻞ ﻧﺼﺐ ﺑﺮ ﮔﻮﺷﯽﻫﺎی ﻫﻮﺷﻤﻨﺪ ﺧﺘﻢ ﻣﯽﺷﻮد ﮐﻪ ﺑﺎ اﺳﺘﻔﺎده از آن ﺑﻪراﺣﺘﯽ و ﺑﻪ آﺳﺎﻧﯽ ﻣﯽﺗﻮان ﺑﺮ ﺑﯿﻨﺎﯾﯽ ﮐﺎرﺑﺮان ﻧﻈﺎرت داﺷﺖ.

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 endtoend 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 handcrafted 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 multicriteria decisionmaking 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.