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research-article

Diabetic Retinopathy Classification Using Hybrid Deep Learning Approach

Authors:

Brahami Menaouer,

Zoulikha Dermane,

Nour El Houda Kebir,

Nada MattaAuthors Info & Claims

SN Computer Science, Volume 3, Issue 5

https://doi.org/10.1007/s42979-022-01240-8

Published: 04 July 2022 Publication History

Abstract

During the recent years, diabetic retinopathy (DR) has been one of the most threatening complications of diabetes that leads to permanent blindness. Further, DR mutilates the retinal blood vessels of a patient having diabetes. Accordingly, various artificial intelligence techniques and deep learning have been proposed to automatically detect abnormalities in DR and its different stages from retina images. In this paper, we propose a hybrid deep learning approach using deep convolutional neural network (CNN) method and two VGG network models (VGG16 and VGG19) to diabetic retinopathy detection and classification according to the visual risk linked to the severity of retinal ischemia. Indeed, the classification of DR deals with understanding the images and their context with respect to the categories. The experimental results, performed on 5584 images, which are an ensemble of online datasets, yielded an accuracy of 90.60%, recall of 95% and F1 score of 94%. The main aim of this work is to develop a robust system for detecting and classifying DR automatically.

References

[1]

Al Ayoubi W, Shalash WM, and Abulkhair MF Diabetic retinopathy detection through deep learning techniques: a review Inf Med Unlocked 2020 20 1-11

[2]

Benbassat J and Polak BC Reliability of screening methods for diabetic retinopathy Diabet Med 2009 26 8 783-790

[3]

Bodapati JD, Naralasetti V, Shareef SN, Hakak S, Bilal M, Maddikunta PKR, and Jo O Blended multi-modal deep ConvNet features for diabetic retinopathy severity prediction Electronics 2020 9 6 1-16

[4]

Bora A, Balasubramanian S, Babenko B, Virmani S, Venugopalan S, Mitani A, and Bavishi P Predicting the risk of developing diabetic retinopathy using deep learning Lancet Digit Health 2020 3 1 10-19

[5]

Brahami M, Sabri M, and Matta N Towards a model to improve Boolean knowledge mapping by using text mining and its applications: case study in healthcare Int J Inf Retr Res 2020 10 3 40-58

[6]

Brahami M, Dermane Z, Kebir N-H, Sabri M, and Matta N Coronavirus pneumonia classification using X-ray and CT scan images with deep convolutional neural networks models J Inf Technol Res (JITR) 2022 15 3 1-23

[7]

Brahami M, Kebir N-H, Dermane Z, Sabri M, and Matta N Detection and classification of brain tumors from MRI images using a deep convolutional neural network approach Int J Softw Innov (IJSI) 2022 10 1 1-25

[8]

Brahami M, Abdeldjouad FZ, and Sabri M Multi-class sentiment classification for healthcare tweets using supervised learning techniques Int J Serv Sci Manag Eng Technol 2022 13 1 1-23

[9]

Castellano G, Castiello C, Mencar C, Vessio G (2020) Crowd detection for drone safe landing through fully-convolutional neural networks. In: Proceedings of the international conference on current trends in theory and practice of informatics, Dortmund, Germany, 17–21 February, p. 301–312

[10]

Chandore V and Asati S Automatic detection of diabetic retinopathy using deep convolutional neural network Int J Adv Res Ideas Innov Technol 2017 3 4 633-641

[11]

Choudhury AR, Bhattacharya D, Debnath A, Biswas A (2019) An integrated image processing and deep learning approach for diabetic retinopathy classification. In: Saha A, Kar N, Deb S, editors. The proceeding of second international conference ICCISIoT 2019, Agartala, India, December 13–14, Communications in Computer and Information Science, Springer, p. 3–15

[12]

Das D, Biswas SK, and Bandyopadhyay S A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning Multimedia Tools Appl 2022

[13]

Doshi D, Shenoy A, Sidhpura D, Gharpure P. Diabetic retinopathy detection using deep convolutional neural networks. In: Proceeding of the international conference on computing, analytics and security trends, Qingdao, China, Published in IEEE; 2016. pp. 261–266.

[14]

Dutta S, Manideep BC, Basha SM, Caytiles RD, and Iyengar NCSN Classification of diabetic retinopathy images by using deep learning models Int J Grid Distrib Comput 2018 11 1 89-106

[15]

El Asnaoui K and Chawki Y Using X-ray Images and Deep Learning for Automated Detection of Coronavirus Disease J Biomol Struct Dyn 2020 38 1-22

[16]

El Houby MF Using transfer learning for diabetic retinopathy stage classification Appl Comput Inf 2021 17 1 1-11

[17]

Erciyas A and Barışçı N An effective method for detecting and classifying diabetic retinopathy lesions based on deep learning Comput Math Methods Med 2021 34 2021 1-13

[18]

Fleming AD, Goatman KA, Philip S, Prescott GJ, and Sharp PF Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts Br J Ophthalmol 2010 94 12 1606-1610

[19]

Gadekallu TR, Khare N, Bhattacharya S, Singh S, Maddikunta PKR, and Srivastava G Deep neural networks to predict diabetic retinopathy J Ambient Intell Human Comput 2020 11 3 1-14

[20]

Gadekallu TR, Khare N, Bhattacharya S, Singh S, Reddy Maddikunta PK, Ra IH, and Alazab M Early detection of diabetic retinopathy using PCA-firefly based deep learning model Electronics 2020 9 2 3-16

[21]

Gao J, Leung C, Miao C. Diabetic retinopathy classification using an efficient convolutional neural network. In: Proceeding of the IEEE international conference on agents (ICA), October 18–21, Jinan, China, 2019. p. 80–85

[22]

Gurani VK, Ranjan A, and Chowdhary CL Diabetic retinopathy detection using neural network Int J Innov Technol Explor Eng 2019 8 10 2936-2940

[23]

Gondal WM, Köhler JM, Grzeszick R, Fink GA, Hirsch M. Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In: Proceeding of the IEEE international conference image process (ICIP'2017), September 17–20, 2017. p. 2069–2073.

[24]

Hemanth DJ, Deperlioglu O, and Kose U An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network Neural Comput Appl 2020 32 707-721

[25]

Hemanth DJ, Deperlioglu O, and Kose U An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network Neural Comput Appl 2019 31 1-15

[26]

Hieu NV and Hien NLH Recognition of plant species using deep convolutional feature extraction Int J Emerg Technol 2020 11 3 904-910

[27]

Jiang Y, Zhang H, Tan N, and Chen L Automatic retinal blood vessel segmentation based on fully convolutional neural networks Symmetry 2019 11 1112 1-22

[28]

Jyotiyana M, Kesswani N. Deep learning and the future of biomedical image analysis. In: Dash S et al, editors. Deep learning techniques for biomedical and health informatics, Studies in big data. vol 68. Springer; 2020. p. 329–345

[29]

Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R. Diabetic retinopathy classification using a modified xception architecture. In: Proceedings of the 2019 IEEE international symposium on signal processing and information technology (ISSPIT'19), Ajman, United Arab Emirates, 10–12 December, 2019. p 1–6

[30]

Khalifa NEM, Loey M, Taha MHN, and Mohamed HNET Deep transfer learning models for medical diabetic retinopathy detection Acta Inf Med 2019 27 5 327-332

[31]

Kumar SPN, Deepak RU, Satharb A, Sahasranamam V, Kumar RR. Automated detection system for diabetic retinopathy using two field fundus photography. In: Proceeding of the 6th international conference on advances in computing & communications (ICACC'16), vol. 93. 6–8 September, Cochin, India, published in Procedia Computer Science; 2016. p. 486–494

[32]

Li F, Liu Z, Chen H, Jiang M, Zhang X, and Wu Z Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm Transl Vis Sci Technol 2019 8 6 1-13

[33]

Lin J, Cai Q, and Lin M Multi-label classification of fundus images with graph convolutional network and self-supervised learning IEEE Signal Process Lett 2021 28 454-458

[34]

Liu H, Yue K, Cheng S, Pan C, Sun J, and Li W Hybrid model structure for diabetic retinopathy classification J Healthc Eng 2020 2020 1-9

[35]

Maeda-Gutiérrez V, Galván-Tejada CE, Zanella-Calzada LA, Celaya-Padilla JM, Galván-Tejada JI, Gamboa-Rosales H, Luna-García H, Magallanes-Quintanar R, Guerrero Méndez CA, and Olvera-Olvera CA Comparison of convolutional neural network architectures for classification of tomato plant diseases Appl Sci 2020 10 1245 1-15

[36]

Mateen M, Wen J, Nasrullah SS, and Huang Z Fundus image classification using VGG-19 architecture with PCA and SVD Symmetry 2019 11 1 1-12

[37]

Nair M and Mishra DS Categorization of diabetic retinopathy severity levels of transformed images using clustering approach Int J Comput Sci Eng 2019 7 1 642-648

[38]

Nair M and Mishra D Classification of diabetic retinopathy severity levels of transformed images using K-means and thresholding method Int J Eng Adv Technol 2019 8 4 51-59

[39]

Ni J, Chen Q, Liu C, Wang H, Cao Y, Liu B. An effective CNN approach for diabetic retinopathy stage classification with dual inputs and selective data sampling. In: Proceeding of the 18th IEEE international conference on machine learning and applications (ICMLA'2019), 2019. p. 1578–1584.

[40]

Pak A, Ziyaden A, Tukeshev K, Jaxylykova A, and Abdullina D Comparative analysis of deep learning methods of detection of diabetic retinopathy Cogent Eng 2020 7 1 1-9

[41]

Pan J, Yong Z, Sui D, Qin H. Diabetic retinopathy detection based on deep convolutional neural networks for localization of discriminative regions. In: Proceeding of the 8th international conference on virtual reality and visualization (ICVRV), October 22–24, Qingdao, China, Published in IEEE; 2018. p. 46–52

[42]

Patel S A comprehensive analysis of convolutional neural network models Int J Adv Sci Technol 2020 29 4 771-777

[43]

Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng CY. Convolutional neural networks for diabetic retinopathy. In: Proceeding of the international conference on medical imaging understanding and analysis (MIUA'2016), vol. 90. 6–8 July, Loughborough, UK, Published in Procedia Computer Science; 2016. p. 200–2005.

[44]

Qummar S, Khan FG, Shah S, Khan A, Shamshirband S, Rehman ZU, Khan IA, and Jadoon AW A deep learning ensemble approach for diabetic retinopathy detection IEEE Access 2019 7 150530-150540

[45]

Rodriguez-Leon C, Arevalo W, Banos O, Villalonga C. Deep learning for diabetic retinopathy prediction. In: The proceeding of the international work-conference on artificial neural networks, vol. 12861. June 16–18, Virtual Event, Springer, LNCS 2021. p. 537–546

[46]

Roshini T, Ravi RV, Reema Mathew A, Kadan AB, and Subbian PS Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network Int J Imaging Syst Technol 2020 30 1 1-21

[47]

Sampaul TGA, Robinson YH, Julie EG, Shanmuganathan V, Nam Y, and Rho S Diabetic retinopathy diagnostics from retinal images based on deep convolutional networks Preprints 2020 2020 1-21

[48]

Seth S and Agarwal B A hybrid deep learning model for detecting diabetic retinopathy J Stat Manag Syst 2018 21 4 569-574

[49]

Setiawan W and Damayanti F Layers modification of convolutional neural network for pneumonia detection J Phys Conf Ser 2020 1477 1-10

[50]

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceeding of the 3rd IAPR Asian conference on pattern recognition (ACPR'2015), November 3–6, Kuala Lumpur, Malaysia; 2015. p. 730–734.

[51]

Suganthi SRL, Sneha UK, and Shwetha S Diabetic retinopathy classification using machine learning techniques Int J Eng Trends Technol 2020 68 1 51-56

[52]

Sun Y and Zhang D Diagnosis and analysis of diabetic retinopathy based on electronic health records IEEE Access Spec Sect Healthc Inf Technol Extreme Remote Environ 2019 7 86115-86120

[53]

Supriya M, Seema H, Zia S. Diabetic retinopathy detection using deep learning. In: Proceeding of the international conference on smart technologies in computing, electrical and electronics (ICSTCEE). 9–10 Oct. 2020, Bengaluru, India, 2020. p. 515–520.

[54]

Wang Z, Yin Y, Shi J, Fang W, Li H, Wang X. Zoom-in-net: deep mining lesions for diabetic retinopathy detection. In: International conference on medical image computing and computer-assisted intervention (MICCAI'2017), Canada, September 11–13, Published in Lecture Notes in Computer Science book series, vol. 10433, 2017. p. 267–275.

[55]

Wan S, Liang Y, and Zhang Y Deep convolutional neural networks for diabetic retinopathy detection by image classification Comput Electr Eng 2018 72 274-282

[56]

Wang Z, Yang J. Diabetic retinopathy detection via deep convolutional networks for discriminative localization and visual explanation. In: Proceeding of the workshops of the thirty-second AAAI conference on artificial intelligence (AAAI-18), February 2–7, New Orleans, Louisiana, USA, 2018. p. 514–522.

[57]

Wang X, Lu Y, Wang Y, Chen WB. Diabetic retinopathy stage classification using convolutional neural networks. In: Proceeding of the international conference on information reuse and integration for data science, Salt Lake City, USA, July 7–9, 2018. p. 465–471.

[58]

Zago GT, Andreão RV, Dorizzi B, and Teatini Salles EO Diabetic retinopathy detection using red lesion localization and convolutional neural networks Comput Biol Med 2020 116 1-12

[59]

Zhang X, Zou J, He K, and Sun J Accelerating very deep convolutional networks for classification and detection IEEE Trans Pattern Anal Mach Intell 2016 38 1943-1955

[60]

Zhang Q, Wang H, Yoon SW, Won D, and Srihari K Lung nodule diagnosis on 3D computed tomography images using deep convolutional neural networks Procedia Manuf 2019 39 363-370

Cited By

Menaouer BIslem ANada M(2023)Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural NetworksInternational Journal of Intelligent Information Technologies10.4018/IJIIT.32995619:1(1-22)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.4018/IJIIT.329956
Alajlan ARazaque A(2023)ESOA-HGRU: egret swarm optimization algorithm-based hybrid gated recurrent unit for classification of diabetic retinopathyArtificial Intelligence Review10.1007/s10462-023-10532-156:Suppl 2(1617-1646)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10462-023-10532-1

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Published In

cover image SN Computer Science

SN Computer Science Volume 3, Issue 5

Aug 2022

1292 pages

EISSN:2661-8907

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© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 04 July 2022

Accepted: 02 June 2022

Received: 17 February 2022

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Cited By

Menaouer BIslem ANada M(2023)Android Malware Detection Approach Using Stacked AutoEncoder and Convolutional Neural NetworksInternational Journal of Intelligent Information Technologies10.4018/IJIIT.32995619:1(1-22)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.4018/IJIIT.329956
Alajlan ARazaque A(2023)ESOA-HGRU: egret swarm optimization algorithm-based hybrid gated recurrent unit for classification of diabetic retinopathyArtificial Intelligence Review10.1007/s10462-023-10532-156:Suppl 2(1617-1646)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10462-023-10532-1

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