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Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images

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Abstract

Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the availability of blood vessels and other distractions. To tackle these issues, we proposed a novel exudates classification from the fundus image known as hybrid convolutional neural network (CNN)-based binary local search optimizer–based particle swarm optimization algorithm. The proposed method from this paper exploits image augmentation to enlarge the fundus image to the required size without losing any features. The features from the resized fundus images are extracted as a feature vector and fed into the feed-forward CNN as the input. Henceforth, it classifies the exudates from the fundus image. Further, the hyperparameters are optimized to reduce the computational complexities by utilization of binary local search optimizer (BLSO) and particle swarm optimization (PSO). The experimental analysis is conducted on the public ROC and real-time ARA400 datasets and compared with the state-of-art works such as support vector machine classifiers, multi-modal/multi-scale, random forest, and CNN for the performance metrics. The classification accuracy is high for the proposed work, and thus, our proposed outperforms all the other approaches.

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References

  1. Garg, Seema, and Richard M. Davis. "Diabetic retinopathy screening update." Clinical diabetes  27, no. 4 (2009): 140-145.

    Google Scholar 

  2. Zhang, Xiwei, Guillaume Thibault, Etienne Decencière, Beatriz Marcotegui, Bruno Laÿ, Ronan Danno, Guy Cazuguel et al. "Exudate detection in color retinal images for mass screening of diabetic retinopathy." Medical image analysis 18, no. 7 (2014): 1026-1043.

  3. Khojasteh, Parham, Leandro AparecidoPassos Júnior, Tiago Carvalho, Edmar Rezende, Behzad Aliahmad, Joao Paulo Papa, and Dinesh Kant Kumar. "Exudate detection in fundus images using deeply-learnable features." Computers in biology and medicine 104 (2019): 62-69.

  4. Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers & Security, 77, 277–288.

    Article  Google Scholar 

  5. Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems, 9(3), 117–126.

    Article  Google Scholar 

  6. Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.

    Article  Google Scholar 

  7. Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., MR, Rejeesh. and Regu Sundararaj, 2020. CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Progress in Photovoltaics: Research and Applications, 28(11), pp.1128-1145.

    Article  Google Scholar 

  8. Rejeesh, M.R. and Thejaswini, P., 2020. MOTF: Multi-objective Optimal Trilateral Filtering based partial moving frame algorithm for image denoising. Multimedia Tools and Applications, 79(37), pp.28411-28430.

    Article  Google Scholar 

  9. Kavitha, D. and Ravikumar, S., 2021. IOT and context-aware learning-based optimal neural network model for real‐time health monitoring. Transactions on Emerging Telecommunications Technologies, 32(1), p.e4132.

  10. Sundararaj, V. and Selvi, M., 2021. Opposition grasshopper optimizer based multimedia data distribution using user evaluation strategy. Multimedia Tools and Applications, 80(19), pp.29875-29891.

  11. Hassan BA (2020) CSCF: a chaotic sine cosine firefly algorithm for practical application problems. Neural Comput Appl 1–20

  12. Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V. and Rejeesh, M.R., 2021. An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control, 66, p.102480.

  13. Haseena, K.S., Anees, S. and Madheswari, N., 2014. Power optimization using EPAR protocol in MANET. International Journal of Innovative Science, Engineering & Technology, 6, pp.430-436.

    Google Scholar 

  14. Gowthul Alam MM, Baulkani S (2019b) Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowl Inf Syst 60(2):971–1000

    Article  Google Scholar 

  15. Gowthul Alam MM, Baulkani S (2017) Reformulated query-based document retrieval using optimised kernel fuzzy clustering algorithm. Int J Bus Intell Data Min 12(3):299

    Google Scholar 

  16. Gowthul Alam MM, Baulkani S (2019a) Geometric structure information based multi-objective function to increase fuzzy clustering performance with artificial and real-life data. Soft Comput 23(4):1079–1098

    Article  Google Scholar 

  17. Nisha, S. and Madheswari, A.N., 2016. Secured authentication for internet voting in corporate companies to prevent phishing attacks. International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE), 22(1), pp.45-49.

    Google Scholar 

  18. Privitera, Claudio M., Laura W. Renninger, Thom Carney, Stanley Klein, and Mario Aguilar. "Pupil dilation during visual target detection." Journal of Vision 10, no. 10 (2010): 3-3.

    Article  Google Scholar 

  19. Esmann, V., K. Lundbaek, and P. H. Madsen. "Types of exudates in diabetic retinopathy." Acta Medica Scandinavica 174, no. 3 (1963): 375-384.

    Article  CAS  Google Scholar 

  20. Gregory-Evans, K. (2021). A review of diseases of the retina for neurologists. Handbook of Clinical Neurology, 178, 1-11.

    Article  Google Scholar 

  21. Hammer, Jon David, Henry Nguyen, Jacqueline Palmer, Sarah Rowlinson, and Swati Agarwal-Sinha. "Computerized analysis of retinal vascular growth following intravitreal bevacizumab monotherapy in retinopathy of prematurity until the age of three years." Investigative Ophthalmology & Visual Science 62, no. 8 (2021): 3242-3242.

  22. Manikandan, N. and Pravin, A., 2019. Hybrid resource allocation and task scheduling scheme in cloud computing using optimal clustering techniques. International Journal of Services Operations and Informatics, 10(2), pp.104-121.

    Article  Google Scholar 

  23. Nanjappan, M., Natesan, G. and Krishnadoss, P., 2021. An adaptive neuro-fuzzy inference system and black widow optimization approach for optimal resource utilization and task scheduling in a cloud environment. Wireless Personal Communications, pp.1-26.

    Article  Google Scholar 

  24. Wang, Jie, Chaoliang Zhong, Cheng Feng, Jun Sun, and Yasuto Yokota. "Feature Disentanglement For Cross-Domain Retina Vessel Segmentation." In 2021 IEEE International Conference on Image Processing (ICIP), pp. 26-30. IEEE, 2021.

  25. Ashame, Laurine A., Sherin M. Youssef, and Salema F. Fayed. "Abnormality Detection in Eye Fundus Retina." In 2018 International Conference on Computer and Applications (ICCA), pp. 285-290. IEEE, 2018.

  26. Tang, Li, Meindert Niemeijer, Joseph M. Reinhardt, Mona K. Garvin, and Michael D. Abramoff. "Splat feature classification with application to retinal hemorrhage detection in fundus images." IEEE Transactions on Medical Imaging 32, no. 2 (2012): 364-375.

    Article  Google Scholar 

  27. Deka, Dharitri, Jyoti Prakash Medhi, and S. R. Nirmala. "Detection of macula and fovea for disease analysis in color fundus images." In 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), pp. 231-236. IEEE, 2015.

  28. GeethaRamani, R. and Balasubramanian, L., 2018. Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening. Computer methods and programs in biomedicine, 160, pp.153-163.

    Article  Google Scholar 

  29. Lee, Kyoung Min, Sun-Won Park, Martha Kim, Sohee Oh, and Seok Hwan Kim. "Relationship between Three-Dimensional Magnetic Resonance Imaging Eyeball Shape and Optic Nerve Head Morphology." Ophthalmology 128, no. 4 (2021): 532-544.

  30. Orujov, F., RytisMaskeliūnas, RobertasDamaševičius, and W. Wei. "Fuzzy based image edge detection algorithm for blood vessel detection in retinal images." Applied Soft Computing 94 (2020): 106452.

  31. Pathan, Sumaiya, Preetham Kumar, Radhika Pai, and Sulatha V. Bhandary. "Automated detection of optic disc contours in fundus images using decision tree classifier." Biocybernetics and Biomedical Engineering 40, no. 1 (2020): 52-64.

    Article  Google Scholar 

  32. AbdelMaksoud, Eman, Sherif Barakat, and Mohammed Elmogy. "A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection." Computers in Biology and Medicine 126 (2020): 104039.

  33. Khojasteh, Parham, Behzad Aliahmad, and Dinesh Kant Kumar. "A novel color space of fundus images for automatic exudates detection." Biomedical Signal Processing and Control 49 (2019): 240-249.

  34. Syed, Adeel M., M. Usman Akram, Tahir Akram, Muhammad Muzammal, Shehzad Khalid, and Muazzam Ahmed Khan. "Fundus images-based detection and grading of macular edema using robust macula localization." IEEE Access 6 (2018): 58784-58793.

  35. Fraz, M. Moazam, Waqas Jahangir, Saqib Zahid, Mian M. Hamayun, and Sarah A. Barman. "Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification." Biomedical Signal Processing and Control 35 (2017): 50-62.

  36. Wang, Hui, Guohui Yuan, Xuegong Zhao, Lingbing Peng, Zhuoran Wang, Yanmin He, Chao Qu, and Zhenming Peng. "Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening." Computer methods and programs in biomedicine 191 (2020): 105398.

  37. Ou, Xiang, Wei Pan, and Perry Xiao. In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). International journal of pharmaceutics 460, no. 1-2 (2014): 28-32.

    Article  CAS  Google Scholar 

  38. Xu, Kele, Dawei Feng, and Haibo Mi. "Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image." Molecules 22, no. 12 (2017): 2054.

    Article  Google Scholar 

  39. Dhaliwal, Jatinder Singh, and J. S. Dhillon. "A synergy of binary differential evolution and binary local search optimizer to solve multi-objective profit based unit commitment problem." Applied Soft Computing 107 (2021): 107387.

  40. Wang, Dongshu, Dapei Tan, and Lei Liu. "Particle swarm optimization algorithm: an overview." Soft Computing 22, no. 2 (2018): 387-408.

    Article  Google Scholar 

  41. Agarwalla, Prativa, and Sumitra Mukhopadhyay. "Efficient player selection strategy based diversified particle swarm optimization algorithm for global optimization." Information Sciences 397 (2017): 69-90.

    Article  Google Scholar 

  42. https://ieee-dataport.org/open-access/diabetic-retinopathy-fundus-imagedatasetagar300

  43. Long, Shengchun, Xiaoxiao Huang, Zhiqing Chen, ShahinaPardhan, and Dingchang Zheng. "Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation." BioMed research international 2019 (2019).

  44. Wisaeng, Kittipol, and Worawat Sa-Ngiamvibool. "Exudates detection using morphology mean shift algorithm in retinal images." IEEE Access 7 (2019): 11946-11958.

    Article  Google Scholar 

  45. Pratheeba, C., and N. Nirmal Singh. "A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier." Journal of medical systems 43, no. 7 (2019): 1-16.

  46. Tan, J.H., Fujita, H., Sivaprasad, S., Bhandary, S.V., Rao, A.K., Chua, K.C. and Acharya, U.R., 2017. Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Information sciences, 420, pp.66-76.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, St. Joseph’s College of Engineering, OMR, 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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Correspondence to J. Ramya.

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Ramya, J., Rajakumar, M.P. & Maheswari, B.U. Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images. J Digit Imaging 35, 56–67 (2022). https://doi.org/10.1007/s10278-021-00534-2

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