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Computer vision model with novel cuckoo search based deep learning approach for classification of fish image

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Abstract

Fish is one of the most important cold-blooded animal groups. Fish is an important part of a healthy diet since it contains several minerals and micronutrients that are necessary for general body development. Because different kinds of fish have varied symptoms when it comes to sickness and decay, it’s critical that we be able to identify and classify the most essential fish species. Traditional methods in this domain are now tedious and slow, however systems based on better deep learning techniques can overcome them. This study proposed a Deep Learning Artificial Neural Network (DLANN) model with a novel optimization technique for fish image classification. The success of DLANN is primarily attributed to its architecture, the optimization technique used, and the tuning of hyperparameters to identify different patterns in data. The Cuckoo Search (CS) algorithm is a popular nature-inspired optimization technique used to solve real-time science and engineering problems. In this paper, to overcome the shortcoming of CS by introducing a Genetic Algorithm (GA) in the exploration phase of the CS approach. A new optimization technique (GA-CS) has been proposed for DLANN to solve problems in fish image classification. An extensive experiment was conducted to compare the performance of the proposed techniques with several popular (EfficientNet, Inception V3, ResNet150 V2, VGG-19, DenseNet 121, LSTM Model, and a personalized Convolutional Neural Network (CNN) model) techniques of deep learning. Experimental results with different evaluation matrices (classification accuracy, recall, precision, standard deviation, and F1- Scores) show that the proposed optimization technique with deep learning gives the best result for fish image classification.

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References

  1. Allken V, Handegard NO, Rosen S et al (2018) Fish species identification using a convolutional neural network trained on synthetic data. ICES J Mar Sci 76(1):342–349

    Article  Google Scholar 

  2. Almero VJD, Concepcion RS (2020) An image classifier for underwater fish detection using classification tree-artificial neural network hybrid, in 2020 RIVF international conference on computing and communication technologies (RIVF) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/abstract/document/9140795/

  3. Aziz RM (2022) Application of nature inspired soft computing techniques for gene selection: a novel frame work for classification of cancer. Soft Comput 1–18

  4. Aziz R, Verma C, Srivastava N (2015) A weighted-SNR feature selection from independent component subspace for nb classification of microarray data. Int J Adv Biotec Res 6:245–255

    Google Scholar 

  5. Aziz R, Srivastava N, Verma CK (2015) T-independent component analysis for svm classification of dna-microarray data. Int J Bioinform Res, ISSN:0975–3087

  6. Aziz R, Verma CK, Srivastava N (2016) A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data. Genom Data 8:4–15

    Article  Google Scholar 

  7. Aziz R, Verma CK, Srivastava N (2017) A novel approach for dimension reduction of microarray. Comput Biol Chem 71:161–169

  8. Aziz R, Verma CK, Jha M, Srivastava N (2017) Artificial neural network classification of microarray data using new hybrid gene selection method. International Journal of Data Mining and Bioinformatics 17(1):42–65

    Article  Google Scholar 

  9. Aziz R, Verma CK, Srivastava N (2018 Dec) Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Annals of Data Science 5(4):615–635

  10. Aziz RM, Baluch MF, Patel S et al (2022) LGBM: a machine learning approach for Ethereum fraud detection. Int J Inf Technol 14(1):1–11

    Google Scholar 

  11. Aziz RM, Hussain A, Sharma P, Kumar P (2022) Machine learning-based soft computing regression analysis approach for crime data prediction. Karb Int J Mod Sci 8(1):1–19

  12. Cristin R, et al. (2020) Deep neural network-based Rider-Cuckoo Search Algorithm for plant disease detection. Artif Intell Rev, : p. 1–26

  13. Desai NP, Baluch MF, Makrariya A et al (2022) Image processing model with deep learning approach for fish species classification. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 13(1):85–99

    Google Scholar 

  14. Fink O, Wang Q, Svensen M et al (2020) Potential, challenges and future directions for deep learning in prognostics and health management applications. Eng Appl Artif Intell 92:103678

    Article  Google Scholar 

  15. Han Y, Chang Q, Ding S et al (2021) Research on multiple jellyfish classification and detection based on deep learning. Multimed Tools Appl 12(3):1–6

    Google Scholar 

  16. Hridayami P, Putra IKG, Wibawa KS (2019) Fish species recognition using VGG16 deep convolutional neural network. Journal of Computing Science and Engineering 13(1):124–130

    Article  Google Scholar 

  17. Hulse SV, Renoult JP, Mendelson et al (2022) Using deep neural networks to model similarity between visual patterns: application to fish sexual signals. Ecological Informatics 67:101486

    Article  Google Scholar 

  18. Iqbal MA, Wang Z, Ali ZA, Riaz S (2021) Automatic fish species classification using deep convolutional neural networks. Wirel Pers Commun 116(2021):1043–1053

    Article  Google Scholar 

  19. Jalal A, Salman A, Mian A, Shortis M, Shafait F (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform 57:101088

    Article  Google Scholar 

  20. Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80(5):8091–8126

  21. Kim YW, Krishna AV (2020) A study on the effect of canny edge detection on downscaled images. Pattern Recognition and Image Analysis 30(3):372–381

    Article  Google Scholar 

  22. Kratzert F, Mader H, (2018) Fish species classification in underwater video monitoring using Convolutional Neural Networks, preprint https://eartharxiv.org/repository/view/1347/

  23. Y. Kutlu, B. İșcimen, A. Uyan, C. Turan (2015) Classification of fish species with two dorsal fins using centroid-contour distance, in: 2015 23nd Signal Processing and Communications Applications Conference (SIU), http://ieeexplore.ieee.org, : pp. 1981–1984

  24. Li X, Shang M, Qin H, et al. (2015) Fast accurate fish detection and recognition of underwater images with fast R-CNN, in: OCEANS 2015 - MTS/IEEE Washington, http://ieeexplore.ieee.org, 2015: pp. 1–5

  25. Lopez-Vazquez V, Lopez-Guede JM, Marini S et al (2020) Video image enhancement and machine learning pipeline for underwater animal detection and classification at cabled observatories. Sensors. 20(2020):714–722

    Google Scholar 

  26. Malik S, Kumar T, Sahoo AK (2017) Image processing techniques for identification of fish disease. In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), pp. 55–59

  27. Martin JM, Bertram MG, Saaristo M,Ecker TE, Hannington SL, Tanner JL, Michelangeli M, O'Bryan MK, Wong BB, (2019) Impact of the widespread pharmaceutical pollutant fluoxetine on behaviour and sperm traits in a freshwater fish. Sci Total Environ 650:1771–1778

  28. Mathur M, Vasudev D, Sahoo S, Jain D, Goel N (2020) Crosspooled FishNet: transfer learning-based fish species classification model. Multimed Tools Appl 79(41):31625–31643

  29. Musheer RA, Verma CK, Srivastava N (2019) Novel machine learning approach for classification of high-dimensional microarray data. Soft Comput 23:13409–13421

    Article  Google Scholar 

  30. Peng H, Zeng Z, Deng C, Wu Z, (2021) Multi-strategy serial cuckoo search algorithm for global optimization. Knowl-Based Syst 214:106729

  31. Pornpanomchai C, Lurstwut B, Leerasakultham P (2013) Shape- and texture-based fish image recognition system. Agriculture and Natural Resources 47(2013):624–634

    Google Scholar 

  32. Rachmatullah MN, Supriana I (2018) Low Resolution Image Fish Classification Using Convolutional Neural Network, in: 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), http://ieeexplore.ieee.org, : pp. 78–83

  33. Saberioon M, Císař P, Labbé L, Souček P, Pelissier P, Kerneis T (2018) Comparative performance analysis of support vector machine, random forest, logistic regression and k-nearest neighbours in rainbow trout (Oncorhynchus mykiss) classification using image-based features. Sensors. 18:1027

    Article  Google Scholar 

  34. Salman A, Maqbool S, Khan AH, Jalal A, Shafait F (2019) Real-time fish detection in complex backgrounds using probabilistic background modelling. Ecological Informatics 51(2019):44–51

    Article  Google Scholar 

  35. Salman A, Siddiqui SA, Shafait F, Mian A, Shortis MR, Khurshid K, Ulges A, Schwanecke U (2019) Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system. ICES J Mar Sci 77(4):1295–1307

  36. Sawant S, Manoharan P (2021 Jan) A hybrid optimization approach for hyperspectral band selection based on wind driven optimization and modified cuckoo search optimization. Multimed Tools Appl 80(2):1725–1748

  37. Sung M, Yu S-C, Girdhar Y (2017) Vision based real-time fish detection using convolutional neural network, in: IEEE (OCEANS )2017 - Aberdeen, 2017: pp. 1–6

  38. Tharwat A, Hemedan AA, Hassanien AE, Gabel T (2018) A biometric-based model for fish species classification. Fish Res 204(2):324–336

    Article  Google Scholar 

  39. Villon S, Mouillot D, Chaumont M, Darling ES, Subsol G, Claverie T, Villéger S (2018) A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecological informatics 48(1):238–244

    Article  Google Scholar 

  40. Xiao M, Liao Y, Bartos P, Filip M, Geng G, Jiang Z (2021) Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm. Multimed Tools Appl 81(2):1567–1587

  41. Yang X, Zhang S, Liu J, Gao Q, Dong S, Zhou C (2021) Deep learning for smart fish farming: applications, opportunities and challenges. Rev Aquac 13(1):66–90

    Article  Google Scholar 

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Acknowledgements

All the authors of this manuscript acknowledge Mr. Kartik Kumar for some technical help to revise the manuscript.

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  1. VIT Bhopal University, Bhopal- Indore Highway, Kothrikalan, Sehore, MP, 466116, India

    Rabia Musheer Aziz, Nishq Poorav Desai & Mohammed Farhan Baluch

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  1. Rabia Musheer Aziz
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  2. Nishq Poorav Desai
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  3. Mohammed Farhan Baluch
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Correspondence to Rabia Musheer Aziz.

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Aziz, R.M., Desai, N.P. & Baluch, M.F. Computer vision model with novel cuckoo search based deep learning approach for classification of fish image. Multimed Tools Appl 82, 3677–3696 (2023). https://doi.org/10.1007/s11042-022-13437-3

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