Skip to main content

Advertisement

SpringerLink
Log in

Diabetic retinopathy detection and classification using CNN tuned by genetic algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The Proposed work intends to automate the detection and classification of diabetic retinopathy from retinal fundus image which is very important in ophthalmology. Most of the existing methods use handcrafted features and those are fed to the classifier for detection and classification purpose. Recently convolutional neural network (CNN) is used for this classification problem but the architecture of CNN is manually designed. In this work, a genetic algorithm based technique is proposed to automatically determine the parameters of CNN and then the network is used for classification of diabetic retinopathy. The proposed CNN model consists of a series of convolution and pooling layer used for feature extraction. Finally support vector machine (SVM) is used for classification. Hyper-parameters like number of convolution and pooling layer, number of kernel and kernel size of convolution layer are determined by using the genetic algorithm. The proposed methodology is tested on publicly available Messidor dataset. The proposed method has achieved accuracy of 0.9867 and AUC of 0.9933. Experimental result shows that proposed auto-tuned CNN performs significantly better than the existing methods. Use of CNN takes away the burden of designing the image features and on the other hand genetic algorithm based methodology automates the design of CNN hyper-parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Hardy KJ, Scarpello J, Foster DH, Moreland JD (1994) Effect of diabetes associated increases in lens optical density on colour discrimination in insulin dependent diabetes. British journal of ophthalmology 78(10):754

    Article  Google Scholar 

  2. Hall A (2011) Recognising and managing diabetic retinopathy. Community eye health 24(75):5

    Google Scholar 

  3. Patel P, Sharma K, Gaudani H (2016) In: National Conference on Emerging Research Trends in Engineering (NCERTE) (IEEE), pp 406–412

  4. Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A et al (2014) Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology 33(3):231

    Article  Google Scholar 

  5. Baliarsingh S, Vipsita S (2019) A chaotic emperor penguin optimized extreme learning machine for microarray cancer classification. IET Systems Biology 14:85. https://doi.org/10.1049/iet-syb.2019.0028

    Article  Google Scholar 

  6. Baliarsingh S, Vipsita S, Gandomi A, Panda A, Bakshi S, Ramasubbareddy S (2020) Analysis of high-dimensional genomic data using mapreduce based probabilistic neural network. Computer Methods and Programs in Biomedicine 195:105. https://doi.org/10.1016/j.cmpb.2020.105625

    Article  Google Scholar 

  7. Baliarsingh S, Muhammad K, Bakshi S (2020) Sara: A memetic algorithm for high-dimensional biomedical data. Applied Soft Computing 101:107. https://doi.org/10.1016/j.asoc.2020.107009

    Article  Google Scholar 

  8. Tavakoli M, Shahri RP, Pourreza H, Mehdizadeh A, Banaee T, Toosi MHB (2013) A complementary method for automated detection of microaneurysms in fluorescein angiography fundus images to assess diabetic retinopathy. Pattern Recognition 46(10):2740

    Article  Google Scholar 

  9. Lazar I, Hajdu A (2012) Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE transactions on medical imaging 32(2):400

    Article  Google Scholar 

  10. Antal B, Hajdu A, Maros-Szabo Z, Török Z, Csutak A, Pető T (2012) A two-phase decision support framework for the automatic screening of digital fundus images. Journal of Computational Science 3(5):262

    Article  Google Scholar 

  11. Rocha A, Carvalho T, Jelinek HF, Goldenstein S, Wainer J (2012) Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE transactions on biomedical engineering 59(8):2244

    Article  Google Scholar 

  12. Yazid H, Arof H, Isa HM (2012) Exudates segmentation using inverse surface adaptive thresholding. Measurement 45(6):1599

    Article  Google Scholar 

  13. Sopharak A, Uyyanonvara B, Barman S (2009) Automatic exudate detection for diabetic retinopathy screening. Science Asia 35(1):80

    Article  Google Scholar 

  14. Wankhade MB, Gurjar A (2016) Analysis of disease using retinal blood vessels detection. Int J Eng Comput Sci 5:12

  15. Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Computerized medical imaging and graphics 32(8):720

    Article  Google Scholar 

  16. Köse C, ŞEvik U, İKibaş C, Erdöl H (2012) Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Computer Methods and Programs in Biomedicine 107(2):274

    Article  Google Scholar 

  17. Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abramoff MD (2012) Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Transactions on Medical Imaging 32(2):364

    Article  Google Scholar 

  18. JayaKumari C, Maruthi R (2012) Detection of hard exudates in color fundus images of the human retina. Procedia Engineering 30:297

    Article  Google Scholar 

  19. Kamble VV, Kokate RD (2020) Automated diabetic retinopathy detection using radial basis function. Procedia Computer Science 167:799

    Article  Google Scholar 

  20. Umesh L, Mrunalini M, Shinde S (2016) Review of image processing and machine learning techniques for eye disease detection and classification. International Research Journal of Engineering and Technology 3(3):547

    Google Scholar 

  21. Parul NS (2015) A study on retinal disease classification and filteration approaches. International journal of computer science and mobile computing 4(5):158

    Google Scholar 

  22. Vijayan T, Sangeetha M, Kumaravel A, Karthik B (2020) Gabor filter and machine learning based diabetic retinopathy analysis and detection, Microprocessors and Microsystems. p 103353

  23. Fageeri SO, Ahmed SMM, Almubarak SA, Mu’azu AA (2017) In: 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE) (IEEE), pp 1–6

  24. Agurto C, Murray V, Barriga E, Murillo S, Pattichis M, Davis H, Russell S, Abràmoff M, Soliz P (2010) Multiscale am-fm methods for diabetic retinopathy lesion detection. IEEE transactions on medical imaging 29(2):502

    Article  Google Scholar 

  25. Barriga ES, Murray V, Agurto C, Pattichis M, Bauman W, Zamora G, Soliz P (2010) In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (IEEE), pp 1349–1352

  26. Priya R, Aruna P (2013) Diagnosis of diabetic retinopathy using machine learning techniques. ICTACT Journal on soft computing 3(4):563

    Article  Google Scholar 

  27. Sánchez CI, Niemeijer M, Dumitrescu AV, Suttorp-Schulten MS, Abramoff MD, van Ginneken B (2011) Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data. Investigative ophthalmology & visual science 52(7):4866

    Article  Google Scholar 

  28. Araújo T, Aresta G, Mendonça L, Penas S, Maia C, Carneiro Â, Mendonça AM, Campilho A (2020) Data augmentation for improving proliferative diabetic retinopathy detection in eye fundus images. IEEE Access 8:182462

    Article  Google Scholar 

  29. Roychowdhury S, Koozekanani DD, Parhi KK (2013) Dream: diabetic retinopathy analysis using machine learning. IEEE journal of biomedical and health informatics 18(5):1717

    Article  Google Scholar 

  30. Quellec G, Lamard M, Abràmoff MD, Decencière E, Lay B, Erginay A, Cochener B, Cazuguel G (2012) A multiple-instance learning framework for diabetic retinopathy screening. Medical image analysis 16(6):1228

    Article  Google Scholar 

  31. Costa P, Campilho A (2017) Convolutional bag of words for diabetic retinopathy detection from eye fundus images. IPSJ Transactions on Computer Vision and Applications 9(1):1

    Article  Google Scholar 

  32. Hacisoftaoglu RE, Karakaya M, Sallam AB (2020) Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems. Pattern Recognition Letters 135:409

    Article  Google Scholar 

  33. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition, Preprint at arXiv:1409.1556

  34. Krizhevsky A, Sutskever I, Hinton GE (2012) In: Advances in neural information processing systems, pp 1097–1105

  35. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  36. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  37. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  38. He K, Zhang X, Ren S, Sun J (2016) In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  39. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning, Preprint at arXiv:1602.07261

  40. Qomariah DUN, Tjandrasa H, Fatichah C (2019) In: 2019 12th International Conference on Information & Communication Technology and System (ICTS) (IEEE), pp 152–157

  41. Cortes C, Vapnik V (1995) Support-vector networks. Machine learning 20(3):273

    MATH  Google Scholar 

  42. Vert JP, Tsuda K, Schölkopf B (2004) A primer on kernel methods. Kernel methods in computational biology 47:35

    Google Scholar 

  43. Jebari K, Madiafi M (2013) Selection methods for genetic algorithms. International Journal of Emerging Sciences 3(4):333

    Google Scholar 

  44. Leng J, Li T, Bai G, Dong Q, Dong H (2016) In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp 1027–1034. https://doi.org/10.1109/ICTAI.2016.0158

  45. Jiang S, Hartley R, Fernando B (2018) In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp 1–7. https://doi.org/10.1109/DICTA.2018.8615840

  46. Kalpic D, Hlupic N, Lovric M (2011) Student’s t-Tests (Springer Berlin Heidelberg, Berlin, Heidelberg), pp 1559–1563. https://doi.org/10.1007/978-3-642-04898-2_641.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sayan Das & Sanjoy Kumar Saha

Authors
  1. Sayan Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanjoy Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, S., Saha, S.K. Diabetic retinopathy detection and classification using CNN tuned by genetic algorithm. Multimed Tools Appl 81, 8007–8020 (2022). https://doi.org/10.1007/s11042-021-11824-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11824-w

Keywords

Search

Navigation