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Automated detection of diabetic retinopathy using optimized convolutional neural network

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Abstract

Diabetes is one of the most common diseases across the world. It affects numerous parts of our body. Diabetic Retinopathy has an effect on retina which causes Diabetes Mellitus (DM) and it may even lead to blindness. Hence, detecting Diabetic Retinopathy (DR) is important during the early stages of diabetes which can prevent the patients from blindness. The publically accessible dataset of Diabetic Retinopathy (DR) contains numerous images of the retina and its results on Diabetic Retinopathy (DR). Our proposed ideology is to classify the images of the retina using an optimized convolutional neural network (OpCoNet) to detect whether the Diabetic Retinopathy (DR) is proliferative or severe or moderate or mild or normal. The optimized convolutional neural network has enhanced feature extraction and classification mechanism. Gray wolf optimization is used to train the CNN layers. The tested model is compared with the existing methodologies used for the detection of Diabetic Retinopathy (DR). The proposed technique effectually provides an accuracy of 98% and sensitivity of 98.5%. The automatic detection of Diabetic Retinopathy (DR) efficaciously proved in the screening process as well as lessens the trouble on medical services support.

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Data Availability

The data is available at: https://www.kaggle.com/competitions/diabetic-retinopathy-detection/data.

References

  1. Achary R, Ng E, Suri J (2008) Image modeling of the human eye. Artech House

  2. Doshi D, Shenoy A, Sidhpura D, Gharpure P (2016) Diabetic retinopathy detection using deep convolutional neural networks. 2016 International Conference on Computing, Analytics and Security Trends (CAST), pp 261–266

  3. Early Treatment Diabetic Retinopathy Study Research Group (1991) Grading diabetic retinopathy from stereoscopic color fundus photographs—an extension of the modified Airlie House classification: ETDRS report number 10. Ophthalmology 98(5):786–806

    Article  Google Scholar 

  4. Gardiner TA, Stitt AW (2022) Juxtavascular microglia scavenge dying pericytes and vascular smooth muscle cells in diabetic retinopathy. Int J Transl Med 2:41–50

    Google Scholar 

  5. Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969

    Article  PubMed  Google Scholar 

  6. Goel T, Murugan R, Mirjalili S et al (2021) OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Appl Intell 51:1351–1366

    Article  Google Scholar 

  7. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J Am Med Assoc (JAMA) 316(22):2402–2410

    Article  Google Scholar 

  8. Hao W, Bie R, Guo J, Meng X, Wang S (2018) Optimized CNN based image recognition through target region selection. Optik 156:772–777. https://doi.org/10.1016/j.ijleo.2017.11.153

    Article  ADS  Google Scholar 

  9. Khan Z et al (2021) Diabetic retinopathy detection using VGG-NIN a deep learning architecture. IEEE Access 9:61408–61416. https://doi.org/10.1109/ACCESS.2021.3074422

    Article  Google Scholar 

  10. Li F, Liu Z, Chen H, Jiang M, Zhang X, Wu Z (2019) Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm. Trans Vis Sci Technol 8(6):4. https://doi.org/10.1167/tvst.8.6.4

    Article  Google Scholar 

  11. Mansour RF (2018) Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed Eng Lett 8(1):41–57

    Article  PubMed  Google Scholar 

  12. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Software’ 69:46–61

    Article  Google Scholar 

  13. Ong GL, Ripley LG, Newsom RS, Cooper M, Casswell AG (2004) Screening for sight-threatening diabetic retinopathy: comparison of fundus photography with automated color contrast threshold test. Am J Ophthalmol 137(3):445–452

    Article  PubMed  Google Scholar 

  14. Quelleca G, Charrière K, Boudi Y, Cochener B, Lamard M (2017) Deep image mining for diabetic retinopathy screening. Med Image Anal. 39:178–193. https://doi.org/10.1016/j.media.2017.04.012

    Article  Google Scholar 

  15. Rahman MT, Dola A (2021) Automated grading of diabetic retinopathy using denseNet-169 architecture. 2021 5th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 1–4. https://doi.org/10.1109/EICT54103.2021.9733431

  16. Rajalakshmi R, Subashini R, Anjana RM, Mohan V (2018) Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye 32(6):1138–1144

    Article  PubMed  PubMed Central  Google Scholar 

  17. Riaz H, Park J, Choi H, Kim H, Kim J (2020) Deep and densely connected networks for classification of diabetic retinopathy. Diagnostics 10(1):24

    Article  PubMed  PubMed Central  Google Scholar 

  18. Sayres R, Taly A, Rahimy E, Blumer K, Coz D et al (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 126(4):552–564

    Article  PubMed  Google Scholar 

  19. Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D et al (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 126:552–564

    Article  PubMed  Google Scholar 

  20. Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4):611–629

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yaqoob MK, Ali SF, Bilal M, Hanif MS, Al-Saggaf UM (2021) ResNet based deep features and random forest classifier for diabetic retinopathy detection. Sensors 21(11):3883. https://doi.org/10.3390/s21113883

    Article  ADS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to show our gratitude to our institution for sharing their pearls of wisdom with us during the course of this research work. We are also immensely grateful to the well-wishers for their comments on an earlier version of the manuscript, although any errors are our own and should not tarnish the reputations of these esteemed individuals.

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  1. Scott Christian College (Autonomous), Tamil Nadu, 629001, India

    S. Jasmine Minija, M. Anline Rejula & B. Shamina Ross

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  1. S. Jasmine Minija
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  2. M. Anline Rejula
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Correspondence to S. Jasmine Minija.

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Minija, S.J., Rejula, M.A. & Ross, B.S. Automated detection of diabetic retinopathy using optimized convolutional neural network. Multimed Tools Appl 83, 21065–21080 (2024). https://doi.org/10.1007/s11042-023-16204-0

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