발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.6] Automated Opthalmic Disease Diagnosis System Using Deep Learning |
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| 논문투고자 |
Hyeonjae Kwon, Sangmin Suh |
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| 논문내용 |
Because the face of population ageing is much faster, early diagnosis of the eye diseases is important. As for the diagnosis, a fundus photography is popular. However, it is very difficult to make exact diagnosis due to blurred images, diagnosis time and human errors, which results in misdiagnosis and blindness in worst case scenario. To solve the problems, machine learning based diagnosis systems were suggested. However, the machine learning based systems inevitably contained expert parameters, like C for margins and gamma for boundary conditions as in support vector machines. Recently, as deep learning methods have been popular, the convolutional neural network (CNN) based image classifications are being used for eye disease diagnosis. However, the previous researches did not use balanced datasets and showed only accuracies for performance verifications. This paper proposes deep learning based eye disease diagnosis systems and balanced datasets are used for better performance. In addition, F1 score and receiver operation characteristic (ROC) curve with area under ROC curve (AUC) including accuracies are measured for exact performance verification. As for classes, normal, diabetic retinopathy, macular degeneration, cataracts, glaucoma, and pathological myopia, i.e., six classes are defined. Moreover, as a dataset, total 4965 data images are used. From the experimental result, we achieved 0.901 as an average F1 score. |
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