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Efficient semi-supervised learning model for limited otolith data using generative adversarial networks

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Abstract

Otolith shape recognition is one of the relevant tool to ensure the sustainability of maritime resources. It is used to study taxonomy, age estimation and discrimination of stocks of fish species. The most performant otolith image classification models are based on convolutional neural network approaches. To build an efficient system, these models require a large number of labeled images, which is hard to obtain. The lack of data became a big challenge, and a real problem of otolith images classification models, it causes the over-fitting issue, which is the main trouble of deep convolutional neural network based models. In this paper, we present a relevant solution for the insufficiency of data. We propose a new semi-supervised classification model based on generative adversarial network. Our results showed that the model is more efficient and also perform better than convolutional neural network system even with a small training dataset. With this efficiency and performance, we found in addition that the accuracy of the model reached 80% on training set of say, 75 images compared to other models such as a convolutional neural network model which accuracy is limited to 60%.

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References

  1. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Ghemawat S (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed

  2. Abu-Mostafa YS (1989) The Vapnik-Chervonenkis dimension: Information versus complexity in learning. Neural Comput 1(3):312–317. https://doi.org/10.1162/neco.1989.1.3.312

    Article  Google Scholar 

  3. Bose AP, Zimmermann H, Winkler G, Kaufmann A, Strohmeier T, Koblmüller S, Sefc KM (2020) Congruent geographic variation in saccular otolith shape across multiple species of African cichlids. Sci Rep 10(1):1–13

    Article  Google Scholar 

  4. Bryant JM (2019) Tribute from the underworld: The historical ecology of the Maya postclassic fish trade with isotopic analysis of otoliths from Mayapán and Caye Coco. State University of New York at Albany

  5. Campana SE, Casselman JM (1993) Stock discrimination using otolith shape analysis. Can J Fish Aquat Sci 50(5):1062–1083

    Article  Google Scholar 

  6. Cerda JM, Palacios-Fuentes P, Díaz-Santana-Iturrios M, Ojeda FP (2021) Description and discrimination of sagittae otoliths of two sympatric labrisomid blennies Auchenionchus crinitus and Auchenionchus microcirrhis using morphometric analyses. J Sea Res 173

  7. Chollet F et al (2015) Keras. Available at: https://github.com/fchollet/keras

  8. Cybenko G (1989) Approximations by superpositions of sigmoidal functions. Math Control Sig Syst 2(4):303–314. https://doi.org/10.1007/BF02551274

    Article  MathSciNet  Google Scholar 

  9. Ding L, Tao J, Ding C, Chen L, Zhang C, Xiang Q, Sun J (2019) Hydrogeomorphic factors drive differences in otolith morphology in fish from the Nu-Salween River. Ecol Freshw Fish 28(1):132–140

    Article  Google Scholar 

  10. Dumoulin V, Belghazi I, Poole B, Lamb A, Ar-Jovsky M, Mastropietro O, Courville A (2016) Adversarially learned inference. arXiv preprint arXiv:1606.00704

  11. El Habouz Y, Es-Saady Y, El Yassa M, Mammass D, Nouboud F, Chalifour A, Manchih K (2016) Recognition of otoliths having a high shape similarity. J Theor Appl Inf Technol 84(1):19

    Google Scholar 

  12. El Habouz Y, Es-Saady Y, Elyassa M, Mammass D, Nouboud F, Chalifour A, Manchih K (2016) Otolith recognition system using a normal angles contour. In: International Conference on Image and Signal Processing (pp 30–39). Springer International Publishing

  13. El Habouz Y, Iggane M, Es-Saady Y, Elyassa M, Manchih K (2018) Deep neural networks for otolith identification. Int J Imag Robot 18(4)

  14. Ghanbarifardi M, Zarei R (2021) Otolith shape analysis of three mudskipper species of Persian Gulf, Iran. J Fish Sci 20(2):333–342

    Google Scholar 

  15. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst

  16. Harbitz A, Albert OT (2015) Pitfalls in stock discrimination by shape analysis of otolith contours. ICES J Mar Sci

  17. Jonsdottir IG, Campana SE, Marteinsdottir G (2006) Otolith shape and temporal stability of spawning groups of Icelandic cod (Gadusmorhua L.). ICES J Mar Sci 63:1501–1512

    Article  Google Scholar 

  18. Keating JP, Brophy D, Officer RA, Mullins E (2014) Otolith shape analysis of blue whiting suggests a complex stock structure at their spawning grounds in the Northeast Atlantic. Fish Res 157:1–6

    Article  Google Scholar 

  19. Kuhl FP, Giardina CR (1982) Elliptic features of a closed contour. Comput Graphics Image Process 18:237–258

    Article  Google Scholar 

  20. Kumar A, Sattigeri P, Fletcher T (2017) Semi-supervised learning with GANs: Manifold invariance with improved inference. In: Advances in Neural Information Processing Systems

  21. Li H (2018) Analysis on the nonlinear dynamics of deep neural networks: Topological entropy and chaos. arXiv:1804.03987

  22. Lombarte A, Lleonart J (1993) Otolith size changes related with body growth, habitat depth and temperature. Environ Biol Fishes 37:297–306

    Article  Google Scholar 

  23. Mahé K, Ider D, Massaro A, Hamed O, Jurado-Ruzafa A, Gonçalves P, Ernande B (2019) Directional bilateral asymmetry in otolith morphology may affect fish stock discrimination based on otolith shape analysis. ICES J Mar Sci 76(1):232–243

    Article  Google Scholar 

  24. Moreira C, Froufe E, Vaz-Pires P, Correia AT (2019) Otolith shape analysis as a tool to infer the population structure of the blue jack mackerel, Trachurus picturatus, in the NE Atlantic. Fish Res 209:40–48

    Article  Google Scholar 

  25. Nasreddine K, Benzinou A, Fablet R (2009) Shape geodesics for the classification of calcified structures: beyond Fourier shape descriptors. Fish Res 98(1):8–15

    Article  Google Scholar 

  26. Neves J, Silva AA, Moreno A, Veríssimo A, Santos AM, Garrido S (2021) Population structure of the European sardine Sardina pilchardus from Atlantic and Mediterranean waters based on otolith shape analysis. Fish Res 243:106050

    Article  Google Scholar 

  27. Odena A (2016) Semi-supervised learning with generative adversarial networks. arXiv:1606.01583

  28. Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: International Conference on Machine Learning (pp 2642–2651). PMLR

  29. Panfili J, De Pontal H, Troadec H, Wrigh PJ (2002) Manual of fish sclerochronology. Ifremer

  30. Pavlov DA (2021) Otolith morphology and relationships of several fish species of the suborder scorpaenoidei. J Ichthyol 61(1):33–47

    Article  Google Scholar 

  31. Platt C, Popper AN (1981) Fine structure and function of the ear. In Hearing and Sound Communication in Fish. Proceeding in Life Sciences, Springer, pp 3–38

    Google Scholar 

  32. Popper A, Lu Z (2000) Structure-function relationships in fish otolith organs. Fish Res 46(1–3):15–25

    Article  Google Scholar 

  33. Reichenbacher B, Kowalke T (2009) Neogene and present-day zoogeography of killifishes (aphanius and aphanolebias) in the mediterranean and paratethys areas. Palaeogeogr Palaeoclimatol Palaeoecol 281(1–2):43–56

    Article  Google Scholar 

  34. Reig-Bolaño R, Marti-Puig P, Rodríguez S, Bajo J, Parisi-Baradad V, Lombarte A (2010) Otoliths identifiers using image contours EFD. In: Distributed Computing and Artificial Intelligence (pp 9–16), Springer Berlin Heidelberg

  35. Rubin M, Stein O, Turko NA et al (2019) TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set. Med Image Anal 57:176–185. https://doi.org/10.1016/j.media.2019.06.014

    Article  Google Scholar 

  36. Sadeghi P, Loghmani M, Afsa E (2019) Trace element concentrations, ecological and health risk assessment in sediment and marine fish Otolithes ruber in Oman Sea, Iran. Mar Pollut Bull 140:248–254

    Article  Google Scholar 

  37. Schmidt W (1969) The otoliths as a means for differentiation between species of fish of very similar appearance. In Proc Symp Oceanog Fish Res Trop Atl, UNESCO, FAO, OAU, p 393396

  38. Schulz-Mirbach T, Ladich F, Plath M, Heß M (2019) Enigmatic ear stones: what we know about the functional role and evolution of fish otoliths. Biol Rev 94(2):457–482

    Article  Google Scholar 

  39. van der Maaten L, Hinton GE (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  40. Vapnik VN, Chervonenkis A (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab App 16:264–280

    Article  Google Scholar 

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Authors and Affiliations

  1. IGDR, Rennes 1 University, Rennes, France

    Youssef El Habouz

  2. TIAD Laboratory, Sciences and Technology Faculty, Sultan Moulay SLimane university, Beni Mellal, Morocco

    Yousef El Mourabit

  3. IRF-SIC, IBN ZOHR University, Agadir, Morocco

    Mbark Iggane

  4. INRH, Agadir, Morocco

    Hammou El Habouz

  5. School of Collective Intelligence, Mohammed VI Polytechnic, Ben Guerir, Morocco

    Gafari Lukumon

  6. LIRIC, UQTR University, Trois-Rivières, Canada

    Fathallah Nouboud

Authors
  1. Youssef El Habouz
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  2. Yousef El Mourabit
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  3. Mbark Iggane
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  4. Hammou El Habouz
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  5. Gafari Lukumon
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  6. Fathallah Nouboud
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Correspondence to Youssef El Habouz.

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El Habouz, Y., El Mourabit, Y., Iggane, M. et al. Efficient semi-supervised learning model for limited otolith data using generative adversarial networks. Multimed Tools Appl 83, 11909–11922 (2024). https://doi.org/10.1007/s11042-023-16007-3

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