Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a c...Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects.However,multiple major eye diseases,such as DR,GLC,and AMD,could not be detected simultaneously by computer-aided systems to date.There were just high-performance-outcome researches on a pair of healthy and eye-diseased group,besides of four categories of fundus image classification.To have a better knowledge of multi-categorical classification of fundus photographs,we used optimal residual deep neural networks and effective image preprocessing techniques,such as shrinking the region of interest,iso-luminance plane contrast-limited adaptive histogram equalization,and data augmentation.Applying these to the classification of three eye diseases from currently available public datasets,we achieved peak and average accuracies of 91.16%and 85.79%,respectively.The specificities for images from the eyes of healthy,GLC,AMD,and DR patients were 90.06%,99.63%,99.82%,and 91.90%,respectively.The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss.This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications.展开更多
基金supported by the KIAT(Korea Institute for Advancement of Technology)grant funded by the Korea Government(MOTIE:Ministry of Trade Industry and Energy)(No.P0012724)the Soonchunhyang University Research Fund.
文摘Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects.However,multiple major eye diseases,such as DR,GLC,and AMD,could not be detected simultaneously by computer-aided systems to date.There were just high-performance-outcome researches on a pair of healthy and eye-diseased group,besides of four categories of fundus image classification.To have a better knowledge of multi-categorical classification of fundus photographs,we used optimal residual deep neural networks and effective image preprocessing techniques,such as shrinking the region of interest,iso-luminance plane contrast-limited adaptive histogram equalization,and data augmentation.Applying these to the classification of three eye diseases from currently available public datasets,we achieved peak and average accuracies of 91.16%and 85.79%,respectively.The specificities for images from the eyes of healthy,GLC,AMD,and DR patients were 90.06%,99.63%,99.82%,and 91.90%,respectively.The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss.This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications.
