Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images

被引：25
|
作者：
Elgafi, Mahmoud [1 ]
Sharafeldeen, Ahmed [2 ]
Elnakib, Ahmed [2 ]
Elgarayhi, Ahmed [1 ]
Alghamdi, Norah S. [3 ]
Sallah, Mohammed [1 ,4 ]
El-Baz, Ayman [2 ]
机构：
[1] Mansoura Univ, Fac Sci, Phys Dept, Appl Math Phys Res Grp, Mansoura 35516, Egypt
[2] Univ Louisville, Dept Bioengn, BioImaging Lab, Louisville, KY 40292 USA
[3] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 11671, Saudi Arabia
[4] Higher Inst Engn & Technol, New Damietta 34517, Egypt
来源：
SENSORS | 2022年 / 22卷 / 20期
关键词：
diabetic retinopathy; neural networks; thickness; OCT; reflectivity; classification; AIDED DIAGNOSIS SYSTEM;
D O I：
10.3390/s22207833
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.
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收藏
页数：13
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