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HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification

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Abstract

Recently, automated retinal image processing has been considered a competitive field of research due to the low-accuracy results, complexity, and unacceptable outcomes associated with it. In this article, we proposed a novel approach for the classification of fundus images from different kinds of fundus disorders. The original images are preprocessed in terms of noise and contrast enhancement by using the contrast limited adaptive histogram equalization method. The optic cup segmentation from the fundus images is effectively handled via the search and rescue optimization algorithm. After that, the color, texture, and shape-based gray-level co-occurrence matrix features are extracted. The hybrid particle swarm optimization with local search strategy improves the DNN parameter and the newly developed method is named as optimal DNN. The optimal DNN is used to classify whether the image is diabetic retinopathy, glaucoma, or age-related macular degeneration. Experimentally, different kinds of datasets such as STARE, Drishti, and RIM-One datasets with performance measure are validated. Finally, the proposed approaches demonstrate higher classification performances in terms of accuracy, specificity, sensitivity, precision, recall, and f-measure.

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References

  1. Shen Y, Sheng B, Fang R, Li H, Dai L, Stolte S, Qin J, Jia W, Shen D (2020) Domain-invariant interpretable fundus image quality assessment. Med Image Anal 61:101654

    Article  Google Scholar 

  2. Shankar K, Zhang Y, Liu Y (2020) Wu and Chen (2020) Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 8:118164–118173

    Article  Google Scholar 

  3. Sakaguchi A, Wu R, Kamata S (2019) Fundus image classification for diabetic retinopathy using disease severity grading. In: Proceedings of the 2019 9th international conference on biomedical engineering and technology, pp 190–196

  4. Mateen M, Wen J, Song S, Huang Z (2019) Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry 11(1):1

    Article  Google Scholar 

  5. Meng Q, Hashimoto Y, Satoh S (2019) Fundus image classification and retinal disease localization with limited supervision. In: Asian conference on pattern recognition, pp 469–482

  6. Ghani A, See CH, Sudhakaran V, Ahmad J, Abd-Alhameed R (2019) Accelerating retinal fundus image classification using artificial neural networks (ANNs) and reconfigurable hardware (FPGA). Electronics 8(12):1522

    Article  Google Scholar 

  7. Qureshi I, Ma J, Shaheed K (2019) A hybrid proposed fundus image enhancement framework for diabetic retinopathy. Algorithms 12(1):14

    Article  Google Scholar 

  8. Choi D, Cho H, Rhee W (2018) On the difficulty of DNN hyperparameter optimization using learning curve prediction. In: TENCON 2018–2018 IEEE region 10 conference, pp 0651–0656

  9. Sundararaj V (2019) Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int J Biomed Eng Technol 31(4):325

    Article  Google Scholar 

  10. Sundararaj V, Muthukumar S, Kumar RS (2018) An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput Secur 77:277–288

    Article  Google Scholar 

  11. Rejeesh MR (2019) Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78(16):22691–22710

    Article  Google Scholar 

  12. Vinu S (2016) An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int J Intell Eng Syst 9(3):117–126

    Google Scholar 

  13. Sundararaj V, Anoop V, Dixit P, Arjaria A, Chourasia U, Bhambri P, Rejeesh MR, Sundararaj R (2020) CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog Photovolt 28(11):1128–1145

    Article  Google Scholar 

  14. Vinu S (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wirel Pers Commun 104(1):173–197

    Article  Google Scholar 

  15. Ravikumar S, Kavitha D (2020) IoT based home monitoring system with secure data storage by Keccak-Chaotic sequence in cloud server. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02424-x

    Article  Google Scholar 

  16. Kavitha, D. and Ravikumar, S., 2020. Designing an IoT based autonomous vehicle meant for detecting speed bumps and lanes on roads. J Ambient Intell Humanized Comput 1–10

  17. Kavitha D, Ravikumar S (2021) IOT and context-aware learning-based optimal neural network model for real-time health monitoring. Trans Emerg Telecommun Technol 32(1):e4132

    Google Scholar 

  18. Ravikumar S, Kavitha D, (2021) IOT based autonomous car driver scheme based on ANFIS and black widow optimization. J Ambient Intell Humanized Comput: 1–14

  19. Albert P, Nanjappan M (2020) An efficient kernel FCM and artificial fish swarm optimization-based optimal resource allocation in cloud. J Circ Syst Comput 29(16):2050253

    Article  Google Scholar 

  20. Juneja M, Thakur N, Thakur S, Uniyal A, Wani A, Jindal P (2020) GC-NET for classification of glaucoma in the retinal fundus image. Mach Vis Appl 31(5):1–18

    Google Scholar 

  21. Sathya N, Rathika N (2020) Different classification methods of fundus image segmentation using quincunx wavelet decomposition. J Ambient Intell Hum Comput 1–7

  22. Pruthi J, Khanna K, Arora S (2020) Optic Cup segmentation from retinal fundus images using Glowworm Swarm Optimization for glaucoma detection. Biomed Signal Process Control 60:102004

    Article  Google Scholar 

  23. Jang Y, Son J, Park KH, Park SJ, Jung K-H (2018) Laterality classification of fundus images using interpretable deep neural network. J Digit Imaging 31(6):923–928

    Article  Google Scholar 

  24. Badgujar and Deore (2019) Hybrid nature inspired SMO-GBM classifier for exudate classification on fundus retinal images. IRBM 40(2):69–77

    Article  Google Scholar 

  25. Shabani A, Asgarian B, Salido M, Gharebaghi SA (2020) Search and Rescue optimization algorithm: a new optimization method for solving constrained engineering optimization problems. Expert Syst Appl 113698

  26. Takahama T, Sakai S (2006) Constrained optimization by the ε constrained differential evolution with gradient-based mutation and feasible elites. In: 2006 IEEE international conference on evolutionary computation, pp 1–8

  27. Ongun H, Leblebicioglu A, Beksaç M, S, Beksaç (2001) Feature extraction and classification of blood cells for an automated differential blood count system. Int Joint Conf Neural Netw Proc 4:2461–2466

    Article  MATH  Google Scholar 

  28. Masoomi Z, Mesgari MS, Hamrah M (2013) Allocation of urban land uses by multi-objective particle swarm optimization algorithm. Int J Geogr Inf Sci 27(3):542–566

    Article  Google Scholar 

  29. Liu Z, Qin Z, Zhu P, Li H (2020) An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems. Eng Appl Artif Intell 95:103771

    Article  Google Scholar 

  30. Niu B, Li L (2008) A novel PSO-DE-based hybrid algorithm for global optimization. In: International conference on intelligent computing, pp 156–163

  31. Mehta SS, Martinez S, Grossman EA, Thayer B, Bullock D, Neary J (2014) Synchronization and switch over methods and systems for an adaptive audio system, U.S. Patent, p 8,838,262

  32. Xiang Z, Miller DJ, Kesidis G (2020) Revealing backdoors, post-training, in DNN classifiers via novel inference on optimized perturbations inducing group misclassification. In: ICASSP 2020–2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 3827–3831

  33. Martínez SZ, Coello CAC (2012) A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms. In: 2012 IEEE congress on evolutionary computation, pp 1–8

  34. Guo Y, Budak Ü, Şengür A, Smarandache F (2017) A retinal vessel detection approach based on shearlet transform and indeterminacy filtering on fundus images. Symmetry 9(10):235

    Article  Google Scholar 

  35. Badar M, Haris M, Fatima A (2020) Application of deep learning for retinal image analysis: a review. Comput Sci Rev 35:100203

    Article  MathSciNet  MATH  Google Scholar 

  36. Fumero F, Alayón S, Sanchez JL, Sigut J, Gonzalez-Hernandez M (2011) RIM-ONE: an open retinal image database for optic nerve evaluation. In 2011 24th International symposium on computer-based medical systems (CBMS), pp 1–6

  37. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics, pp 311–351

  38. Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541

    Article  Google Scholar 

  39. Pan X, Zhang Q, H, Pan, (2020) Improved artificial bee colony algorithm and its application to fundus retinal blood vessel image binarization. IEEE Access 8:123726–123734

    Article  Google Scholar 

  40. Kavithaand M, Palani S (2015) Hierarchical classifier for soft and hard exudates detection of retinal fundus images. J Intell Fuzzy Syst 27(5):2511–2528

    Article  Google Scholar 

  41. Popescu D, Ichim L, Caramihale (2015) Texture based method for automated detection, localization and evaluation of the exudates in retinal images. In: Neural information processing, pp 463–472

  42. Martins J, Cardoso JS, F, Soares (2020) Offline computer-aided diagnosis for Glaucoma detection using fundus images targeted at mobile devices. Comput Methods Programs Biomed 192:105341

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, India

    J. Ramya, M. P. Rajakumar & B. Uma Maheswari

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  1. J. Ramya
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  2. M. P. Rajakumar
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  3. B. Uma Maheswari
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Correspondence to J. Ramya.

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Ramya, J., Rajakumar, M.P. & Uma Maheswari, B. HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification. Neural Comput & Applic 33, 9677–9690 (2021). https://doi.org/10.1007/s00521-021-05732-1

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