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Open AccessArticle

Deep and Densely Connected Networks for Classification of Diabetic Retinopathy

1
Department of Health Science and Technology, Gachon Advanced Institute for Health Sciences & Technology, Incheon 21999, Korea
2
Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, 350-27, Gum-daero, Gumi 39253, Korea
3
School of Information, University of California, 102 South Hall #4600, Berkeley, CA 94720, USA
4
Department of Biomedical Engineering, Gachon University, 534-2, Hambakmoe-ro, Incheon 21936, Korea
*
Authors to whom correspondence should be addressed.
Diagnostics 2020, 10(1), 24; https://doi.org/10.3390/diagnostics10010024
Received: 11 November 2019 / Revised: 17 December 2019 / Accepted: 23 December 2019 / Published: 2 January 2020
(This article belongs to the Special Issue Artificial Intelligence in Diagnostics)
Diabetes has recently emerged as a worldwide problem, and diabetic retinopathy is an abnormal state associated with the human retina. Due to the increase in daily screen-related activities of modern human beings, diabetic retinopathy is more prevalent among adults, leading to minor and major blindness. Doctors and clinicians are unable to perform early diagnoses due to the large number of patients. To solve this problem, this study introduces a classification model for retinal images that distinguishes between the various stages of diabetic retinopathy. This work involves deploying deep and densely connected networks for retinal image analysis with training from scratch. Dense connections between the convolutional layers of the network are an essential factor to enhance accuracy owing to the deeper supervision between layers. Another factor is the growth rate that further assists our model in learning more sophisticated feature maps regarding retinal images from every stage of the network. We compute the area under the curve, sensitivity, and specificity, particularly for messidor-2 and EyePACS. Compared to existing approaches, our method achieved better results, with an approximate rise rate of 0.01, 0.03, and 0.01, respectively. Therefore, computer-aided programs can help in diagnostic centers as automated detection systems. View Full-Text
Keywords: deep learning; densely connected networks; healthcare diagnosis; diabetic retinopathy; convolutional neural networks; fundus image analysis deep learning; densely connected networks; healthcare diagnosis; diabetic retinopathy; convolutional neural networks; fundus image analysis
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MDPI and ACS Style

Riaz, H.; Park, J.; Choi, H.; Kim, H.; Kim, J. Deep and Densely Connected Networks for Classification of Diabetic Retinopathy. Diagnostics 2020, 10, 24. https://doi.org/10.3390/diagnostics10010024

AMA Style

Riaz H, Park J, Choi H, Kim H, Kim J. Deep and Densely Connected Networks for Classification of Diabetic Retinopathy. Diagnostics. 2020; 10(1):24. https://doi.org/10.3390/diagnostics10010024

Chicago/Turabian Style

Riaz, Hamza; Park, Jisu; Choi, Hojong; Kim, Hyunchul; Kim, Jungsuk. 2020. "Deep and Densely Connected Networks for Classification of Diabetic Retinopathy" Diagnostics 10, no. 1: 24. https://doi.org/10.3390/diagnostics10010024

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