Open AccessComputer-aided diagnosis of retinopathy based on vision transformer
Author(s) -
Zhencun Jiang,
Lingyang Wang,
Qixin Wu,
Yilei Shao,
Meixiao Shen,
Wenping Jiang,
Cuixia Dai
Publication year - 2022
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545822500092
Subject(s) - macular degeneration , convolutional neural network , artificial intelligence , optical coherence tomography , cad , computer science , transformer , blindness , computer aided diagnosis , retinal , computer vision , pattern recognition (psychology) , ophthalmology , optometry , medicine , engineering , voltage , electrical engineering , engineering drawing
Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are two common retinal diseases for elder people that may ultimately cause irreversible blindness. Timely and accurate diagnosis is essential for the treatment of these diseases. In recent years, computer-aided diagnosis (CAD) has been deeply investigated and effectively used for rapid and early diagnosis. In this paper, we proposed a method of CAD using vision transformer to analyze optical coherence tomography (OCT) images and to automatically discriminate AMD, DME, and normal eyes. A classification accuracy of 99.69% was achieved. After the model pruning, the recognition time reached 0.010 s and the classification accuracy did not drop. Compared with the Convolutional Neural Network (CNN) image classification models (VGG16, Resnet50, Densenet121, and EfficientNet), vision transformer after pruning exhibited better recognition ability. Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately.