skip to main content
10.1145/3436829.3436854acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicsieConference Proceedingsconference-collections
research-article

Cascaded Architecture for Classifying the Preliminary Stages of Diabetic Retinopathy

Published: 05 January 2021 Publication History

Abstract

Diabetes Mellitus is one of the modern world's most dominant diseases. This condition leads to a dangerous eye disease called Diabetic Retinopathy (DR), which eventually causes total blindness. The purpose of this research is the early detection of this condition to prevent further complications in the future. Over the past few years, Convolutional Neural Networks (CNNs) became very popular in resolving image processing and object detection problems for huge datasets. A cascaded model was proposed to detect the presence of DR and classify it into 4 stages, taking into consideration using a large dataset. Furthermore, preprocessing techniques such as normalization are applied, and finally, the input images are fed into a multi-layer Convolutional Neural Network. This method was utilized on 61,248 retinal images, which are a portion of the (EyePACS) dataset. It achieved a specificity of 96.1% for detecting the presence of the disease and 63.1% for determining its stage.

References

[1]
"Diabetes," (2019, November 25). © 2020 WHO. http://www.who.int/health-topics/diabetes.
[2]
R. Lee, T. Y. Wong, and C. Sabanayagam. 2015. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye and vision, vol. 2, no. 1, p. 17, 2015.
[3]
"Diabetes facts and figures," (2019, December 13). © 2020 International Diabetes Federation. https://idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html.
[4]
S. Kumar and B. Kumar. 2018. Diabetic retinopathy detection by extracting area and number of microaneurysm from color fundus image. in 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 359--364, IEEE, 2018.
[5]
F. Cisneros-Guzmán, S. Tovar-Arriaga, C. Pedraza, and A. González- Gutierrez. 2019. Classification of diabetic retinopathy based on hard exu- dates patterns, using images processing and svm. In 2019 IEEE Colom- bian Conference on Applications in Computational Intelligence (Col- CACI), pp. 1--5, IEEE, 2019.
[6]
H. Tjandrasa, R. E. Putra, A. Y. Wijaya, and I. Arieshanti. 2013. Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin svm. In 2013 IEEE International Conference on Control System, Computing and Engineering, pp. 376--380, IEEE, 2013.
[7]
E. V. Carrera, A. González, and R. Carrera. 2017. Automated detection of diabetic retinopathy using svm. In 2017 IEEE XXIV International Confer- ence on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1--4, IEEE, 2017.
[8]
S. Sangwan, V. Sharma, and M. Kakkar. 2015. Identification of different stages of diabetic retinopathy. In 2015 International Conference on Computer and Computational Sciences (ICCCS), pp. 232--237, IEEE, 2015.
[9]
P. Junjun, Y. Zhifan, S. Dong, and Q. Hong. 2018. Diabetic retinopathy detection based on deep convolutional neural networks for localization of discriminative regions. In 2018 International Conference on Virtual Reality and Visualization (ICVRV), pp. 46--52, IEEE, 2018.
[10]
A. Jain, A. Jalui, J. Jasani, Y. Lahoti, and R. Karani. 2019. Deep learning for detection and severity classification of diabetic retinopathy. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pp. 1--6, IEEE, 2019.
[11]
A. Kwasigroch, B. Jarzembinski, and M. Grochowski. 2018. Deep cnn based decision support system for detection and assessing the stage of dia- betic retinopathy. In 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 111--116, IEEE, 2018.
[12]
S. Suriyal, C. Druzgalski, and K. Gautam, Mobile assisted diabetic retinopathy detection using deep neural network. In 2018 Global Medi- cal Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), pp. 1--4, IEEE, 2018.
[13]
B. Harangi, J. Toth, and A. Hajdu. 2018. Fusion of deep convolutional neural networks for microaneurysm detection in color fundus images. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3705--3708, IEEE, 2018.
[14]
S. H. Khan, Z. Abbas, S. D. Rizvi, et al.2019. Classification of dia- betic retinopathy images based on customised cnn architecture. In 2019 Amity International Conference On Artificial Intelligence (AICAI), pp. 244--248, IEEE, 2019.
[15]
X. Zeng, H. Chen, Y. Luo, and W. Ye. 2019. Automated diabetic retinopa- thy detection based on binocular siameselike convolutional neural network. IEEE Access, vol. 7, pp. 30744--30753, 2019.
[16]
D. Y. Carson Lam, M. Guo, and T. Lindsey. 2018. Automated detection of dia- betic retinopathy using deep learning. AMIA Summits on Translational Science Proceedings, vol. 2018, p. 147, 2018.
[17]
"Eyepacs," (2020, January), http://www.eyepacs.com.
[18]
"Diabetic retinopathy detection," (2019, October 25), https://www.kaggle.com/c/diabetic-retinopathy-detection/data.
[19]
Y. Zhou and S. Lu. 2011. Discovering abnormal patches and transformations of diabetic retinopathy in big fundus collections," pp. 195--206, 01 2017.

Cited By

View all
  • (2024)Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 frameworkFrontiers in Artificial Intelligence10.3389/frai.2024.13961607Online publication date: 16-Apr-2024
  • (2024)A Classification Method for Diabetic Retinopathy Based on Self-supervised LearningAdvanced Intelligent Computing in Bioinformatics10.1007/978-981-97-5689-6_30(347-357)Online publication date: 5-Aug-2024
  • (2023)A Survey on Deep-Learning-Based Diabetic Retinopathy ClassificationDiagnostics10.3390/diagnostics1303034513:3(345)Online publication date: 18-Jan-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICSIE '20: Proceedings of the 9th International Conference on Software and Information Engineering
November 2020
251 pages
ISBN:9781450377218
DOI:10.1145/3436829
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Ain Shams University: Ain Shams University, Egypt

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Image Processing
  2. Neural Network (CNN)

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICSIE 2020

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 19 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Smart grading of diabetic retinopathy: an intelligent recommendation-based fine-tuned EfficientNetB0 frameworkFrontiers in Artificial Intelligence10.3389/frai.2024.13961607Online publication date: 16-Apr-2024
  • (2024)A Classification Method for Diabetic Retinopathy Based on Self-supervised LearningAdvanced Intelligent Computing in Bioinformatics10.1007/978-981-97-5689-6_30(347-357)Online publication date: 5-Aug-2024
  • (2023)A Survey on Deep-Learning-Based Diabetic Retinopathy ClassificationDiagnostics10.3390/diagnostics1303034513:3(345)Online publication date: 18-Jan-2023
  • (2023)Transfer Learning for Diabetic Retinopathy Detection: A Study of Dataset Combination and Model PerformanceApplied Sciences10.3390/app1309568513:9(5685)Online publication date: 5-May-2023
  • (2022)A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image DatasetDiagnostics10.3390/diagnostics1212308412:12(3084)Online publication date: 7-Dec-2022

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media