research-article

Automatic Screening of Diabetic Retinopathy Images with Convolution Neural Network Based on Caffe Framework

Authors:

Jiancheng DongAuthors Info & Claims

ICMHI '17: Proceedings of the 1st International Conference on Medical and Health Informatics 2017

Pages 90 - 94

https://doi.org/10.1145/3107514.3107523

Published: 20 May 2017 Publication History

Abstract

Objective Diabetic retinopathy (DR) is a serious complication of eye in diabetes mellitus (DM) patients. In order to automatically screen DR, we aim to use convolutional neural network (CNN) to screen DR fundus images automatically.

Methods A total of 10,551 fundus images from Kaggle fundus image dataset were collected for this experiment. Firstly, the images were preprocessed by histogram equalization and image augmentation. Then, the CNN was constructed and trained with Caffe framework. Our designed CNN models were trained by 8,626 images. Finally, the performance of the trained CNN model was validated by classifying 1,925 fundus images into DR and non-DR ones.

Results The performance results indicated that the CNN achieved accuracy of 75.70% in 1,925 test fundus images.

Conclusions CNN model is useful to classify the DR fundus images, thus might be applicable in further DR screening program for larger DM population.

References

[1]

Yau, J. W., Rogers, S. L., and Kawasaki, R., et al. 2012. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care, 35(3), 556--564.

[2]

International Diabetes Federation. IDF Diabetes Atlas Sixth editionposter update 2014 [EB/OL]. [2017-01-07]. http://www.idf.org/diabetesatlas/update.

[3]

Liu, L., Wu, X., and Liu, L., et al. 2012. Prevalence of diabetic retinopathy in mainland china: a meta-analysis. PloS one,7(9), e45264.

[4]

Olafsdottir, E., Andersson, D. K., and Dedorsson, I., et al. 2014.The prevalence of retinopathy in subjects with and without type 2 diabetes mellitus. Acta Ophthalmologica, 92(2), 133--137.

[5]

Polack S., Yorston D., López-Ramos A., et al. 2012. Rapid assessment of avoidable blindness and diabetic retinopathy in Chiapas, Mexico. Ophthalmology, 119(5), 1033--1040.

[6]

Demissie M. G., Correia G. H. D. A., Bento C. 2013.Intelligent road traffic status detection system through cellular networks handover information: An exploratory study. Transportation Research Part C Emerging Technologies, 32(4), 76--88.

[7]

M. Bilgili, B. Sahin. 2013. Wind speed prediction of target station from reference stations data. Energy Sources Part A Recovery Utilization & Environmental Effects, 35(35), 455--466.

[8]

Qiu, M., Song, Y., and Akagi, F. 2016. Application of artificial neural network for the prediction of stock market returns: the case of the japanese stock market. Chaos Solitons & Fractals, 85(September), 1--7.

[9]

Valente, G. F. S., Mendonça, R. C. S., and Pereira, J. A. At al. 2014.Artificial neural network prediction of chemical oxygen demand in dairy industry effluent treated by electrocoagulation. Separation & Purification Technology, 132(132), 627--633.

[10]

Amato, F., López, A., and Peña-Méndez, E. M., et al. 2013. Artificial neural networks in medical diagnosis. Journal of Applied Biomedicine, 11(2), 47--58.

[11]

Gardner GG, Keating D, Williamson TH, et al. 1996.Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol, 80(11), 940--4.

[12]

Goh, J., Tang, L., Saleh, G., and Turk, L. A. 2009. Filtering normal retinal images for diabetic retinopathy screening using multiple classifiers. In 9th International Conference on Information Technology and Applications in Biomedicine (November 04-07, 2009), 1--4.

[13]

Anitha, J., Vijila, C. K. S., and Hemanth, D. J. 2009. An enhanced Counter Propagation Neural Network for abnormal retinal image classification. In World Congress on Nature & Biologically Inspired Computing(Coimbatore,India, December, 09-11, 2009), 1--6.

[14]

Priya R, Aruna P. 2012.SVM and neural network based diagnosis of diabetic retinopathy.International Journal of Computer Applications,41(1), 6--12.

[15]

Lichode RV, Kulkarni PS. 2013. Automatic diagnosis of diabetic retinopathy by hybrid multilayer feed forward neural network.International Journal of Science, Engineering and Technology Research,2(9), 1727--1733.

[16]

Shahin EM, Taha TE, Al-Nuaimy W,et al. 2012. Automated detection of diabetic retinopathy in blurred digital fundus images. In 2012 8th International Computer Engineering Conference (December, 29-30, 2012), 20--25.

[17]

Pratt H, Coenen F, and Broadbent D M, et al. 2016Convolutional Neural Networks for Diabetic Retinopathy. Procedia Computer Science, 90, 200--205.

[18]

Hinton G E, Salakhutdinov R R. 2006. Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504--507.

[19]

Lawrence S, Giles C L, and Tsoi A C, et al. 1997.Face recognition: a convolutional neural-network approach[J]. IEEE Transactions on Neural Networks, 8(1), 98--113.

Digital Library

[20]

Nebauer C. 1998.Evaluation of convolutional neural networks for visual recognition. IEEE Transactions on Neural Networks, 9(4), 685--696.

Digital Library

[21]

Krizhevsky A, Sutskever I, Hinton G E. 2012. ImageNet classification with deep convolutional neural networks. In International Conference on Neural Information Processing Systems (Lake Tahoe, Nevada, December 03-06, 2012 ), 1097--1105.

Digital Library

[22]

Szegedy C, Liu W, Jia Y, et al. 2014. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (2015), 1--9.

Cited By

Anand VKoundal DAlghamdi WAlsharbi B(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
https://doi.org/10.3389/frai.2024.1396160
H N AP S AAli ASurendran A(2024)Image Colorization System: An Implementation using the Caffe Deep Learning Framework2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10467366(1-5)Online publication date: 24-Jan-2024
https://doi.org/10.1109/IITCEE59897.2024.10467366
Farahat ZZrira NSouissi NBennani YBencherif SBenamar SBelmekki MNgote MMegdiche K(2024)Diabetic retinopathy screening through artificial intelligence algorithms: A systematic reviewSurvey of Ophthalmology10.1016/j.survophthal.2024.05.008Online publication date: Jun-2024
https://doi.org/10.1016/j.survophthal.2024.05.008
Show More Cited By

Index Terms

Automatic Screening of Diabetic Retinopathy Images with Convolution Neural Network Based on Caffe Framework
1. Computing methodologies
  1. Machine learning

Recommendations

An Automated Model using Deep Convolutional Neural Network for Retinal Image Classification to Detect Diabetic Retinopathy
ICCA 2020: Proceedings of the International Conference on Computing Advancements

Diabetic Retinopathy is considered as one of the significant reasons for vision impairment. Its identification involves detecting the presence of some features in retinal fundus images by clinicians which is a time and resource consuming procedure and a ...
Fine-tuning of pre-trained convolutional neural networks for diabetic retinopathy screening: a clinical study

Diabetic retinopathy is a serious complication of diabetes, and if not controlled, may cause blindness. Automated screening of diabetic retinopathy helps physicians to diagnose and control the disease in early stages. In this paper, two case studies are ...
A Survey on Automatic Diabetic Retinopathy Screening
Abstract
Diabetes is a chronic disease caused due to the increase in the sugar level in the blood. Diabetes mainly affects heart, blood vessels, kidneys, eyes and nerves. There are mellitus type 1 and 2 diabetes, in which insulin is either not taken by the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMHI '17: Proceedings of the 1st International Conference on Medical and Health Informatics 2017

May 2017

118 pages

ISBN:9781450352246

DOI:10.1145/3107514

Copyright © 2017 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMHI '17

ICMHI '17: International Conference on Medical and Health Informatics 2017

May 20 - 22, 2017

Taichung City, Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
249
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)1

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Anand VKoundal DAlghamdi WAlsharbi B(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
https://doi.org/10.3389/frai.2024.1396160
H N AP S AAli ASurendran A(2024)Image Colorization System: An Implementation using the Caffe Deep Learning Framework2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10467366(1-5)Online publication date: 24-Jan-2024
https://doi.org/10.1109/IITCEE59897.2024.10467366
Farahat ZZrira NSouissi NBennani YBencherif SBenamar SBelmekki MNgote MMegdiche K(2024)Diabetic retinopathy screening through artificial intelligence algorithms: A systematic reviewSurvey of Ophthalmology10.1016/j.survophthal.2024.05.008Online publication date: Jun-2024
https://doi.org/10.1016/j.survophthal.2024.05.008
Muchuchuti SViriri S(2023)Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive ReviewJournal of Imaging10.3390/jimaging90400849:4(84)Online publication date: 18-Apr-2023
https://doi.org/10.3390/jimaging9040084
Sebastian AElharrouss OAl-Maadeed SAlmaadeed N(2023)A Survey on Deep-Learning-Based Diabetic Retinopathy ClassificationDiagnostics10.3390/diagnostics1303034513:3(345)Online publication date: 18-Jan-2023
https://doi.org/10.3390/diagnostics13030345
Almayahi WNajim AAl-Sharhanee K(2023)Diabetic Net for Diabetic Retinopathy Image Classification Using Deep Convolutional Neural Network2023 IEEE 7th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)10.1109/ICITISEE58992.2023.10405015(152-156)Online publication date: 29-Nov-2023
https://doi.org/10.1109/ICITISEE58992.2023.10405015
Kumar SDewangan KYadav K(2023)Feature Extraction and Analysis for Diabetic Retinopathy Classification using Pre-trained Deep Networks2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)10.1109/ICAIIHI57871.2023.10489452(1-8)Online publication date: 29-Dec-2023
https://doi.org/10.1109/ICAIIHI57871.2023.10489452
(2022)A Comprehensive Review on the Diabetic Retinopathy, Glaucoma and Strabismus Detection Techniques Based on Machine Learning and Deep LearningEuropean Journal of Medical and Health Sciences10.34104/ejmhs.022.024040(24-40)Online publication date: 1-Mar-2022
https://doi.org/10.34104/ejmhs.022.024040
Bidwai PGite SPahuja KKotecha K(2022)A Systematic Literature Review on Diabetic Retinopathy Using an Artificial Intelligence ApproachBig Data and Cognitive Computing10.3390/bdcc60401526:4(152)Online publication date: 8-Dec-2022
https://doi.org/10.3390/bdcc6040152
Appati JArmah BOwusu ESoli M(2022)Fundus Image Classification: A Wavelet Feature Descriptor Approach2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC55218.2022.10088415(1-6)Online publication date: 19-Nov-2022
https://doi.org/10.1109/ASSIC55218.2022.10088415
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents