Deep Learning Approach for Retinopathy Identification on Combined Clinical Datasets
Abstract
Abstract—This work presents a system for automatic detection
of various stages of diabetic retinopathy (DR) based on fundus
images of patients. The system was built based on a relatively new
and little-used image database: ”Dataset of fundus images for the
study of diabetic retinopathy” version v3 CastilloBenitez21. The
primary dataset was expanded using clinical fundus photographs
acquired from the Department of Nephrology at Wroclaw Medical
University. The diagnostic system was developed based on
various variants of convolutional neural networks (CNNs) that
were pre-trained on ImageNet data. The CNN classifier, based on
VGG16 with transfer learning, proved to be effective and gave
a global accuracy of 83.15%. The evaluation of discrimination
between the non-DR and the DR state resulted in an accuracy
of 89.7%, with a sensitivity of 94.9%, a specificity of 88.3%, and
a Matthews Correlation Coefficient of 0.7665.
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