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New conditional generative adversarial capsule network for imbalanced classification of human sperm head images

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Abstract

Male infertility negatively affects the lives of infertile couples. Sperm head morphology is an essential factor in evaluating semen in male infertility. The lack of samples with abnormal sperm heads compared to the normal sperm head samples makes the classification of sperm head images an imbalance classification problem. In comparison with other deep networks such as convolutional neural networks, capsule neural networks (CapsNets) provide an optimal platform for designing imbalanced classification models by considering spatial relationships of features. Also, generative adversarial networks (GANs) help to improve the imbalanced classification of images by producing suitable synthetic samples. In this paper, a new architecture based on CapsNets and GANs is proposed and evaluated for imbalanced classification of human sperm head images. The new proposed conditional generative adversarial capsule network outperformed other deep learning networks in the balanced and imbalanced classification of human sperm head images. Based on the comparison between the general methods for increasing the data and the proposed network, the general methods have less robustness to reducing the amount of data than the proposed network. The presented network performed a balanced classification of sperm head images with 97.8% accuracy. Additionally, the proposed network maintained an accuracy of over 80% up to the ratio of minority to majority class of 1:30, indicating that it performed properly in the imbalanced classification of sperm images.

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The data that support the findings of this study are available on request from the corresponding author.

References

  1. W. H. Organization, World health statistics 2010. World Health Organization, 2010.

  2. Baskaran S, Agarwal A, Leisegang K, Pushparaj PN, Selvam MKP, Henkel RJT (2021) An in-depth bibliometric analysis and current perspective on male infertility research. World J Men’s Health 39(2):302

    Article  Google Scholar 

  3. Durairajanayagam DJA (2018) Lifestyle causes of male infertility. Arab J Urol 16(1):10–20

    Article  Google Scholar 

  4. Iqbal I, Mustafa G, Ma JJD (2020) Deep learning-based morphological classification of human sperm heads. Diagnostics 10(5):325

    Article  Google Scholar 

  5. Talarczyk-Desole J, Berger A, Taszarek-Hauke G, Hauke J, Pawelczyk L, Jedrzejczak PJGP (2017) Manual vs. computer-assisted sperm analysis: can CASA replace manual assessment of human semen in clinical practice? Ginekol Pol 88(2):56–60

    Article  Google Scholar 

  6. You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D (2021) Machine learning for sperm selection. Nat Rev Urol 1–17

  7. Imani M, Ghassemian H (2020) An overview on spectral and spatial information fusion for hyperspectral image classification: current trends and challenges. Inf Fusion 59:59–83

    Article  Google Scholar 

  8. Cordón I, García S, Fernández A, Herrera FJK-BS (2018) Imbalance: oversampling algorithms for imbalanced classification in R. Knowl-Based Syst 161:329–341

    Article  Google Scholar 

  9. Shaker F, Monadjemi SA, Alirezaie J, Naghsh-Nilchi AR (2017) A dictionary learning approach for human sperm heads classification. Comput Biol Med 91:181–190

    Article  Google Scholar 

  10. Singh R et al (2020) Imbalanced breast cancer classification using transfer learning. IEEE/ACM Trans Comput Biol Bioinf 18(1):83–93

    MathSciNet  Google Scholar 

  11. Jothi G, Inbarani HH, Azar AT, Koubaa A, Kamal NA, Fouad KMJE (2020) Improved dominance soft set based decision rules with pruning for Leukemia image classification. Electronics 9(5):794

    Article  Google Scholar 

  12. Muhammad K, Khan S, Del Ser J, de Albuquerque VHC (2020) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn Syst 32(2):507–522

    Article  Google Scholar 

  13. López V, Fernández A, García S, Palade V, Herrera F (2013) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 250:113–141

    Article  Google Scholar 

  14. Loyola-González O, Martínez-Trinidad JF, Carrasco-Ochoa JA, García-Borroto MJN (2016) Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases. Neurocomputing 175:935–947

    Article  Google Scholar 

  15. Branco P, Torgo L, Ribeiro RPJACS (2016) A survey of predictive modeling on imbalanced domains. ACM Comput Surveys (CSUR) 49(2):1–50

    Article  Google Scholar 

  16. Vannucci M, Colla V (2017) Genetic algorithms based resampling for the classification of unbalanced datasets. In: International Conference on Intelligent Decision Technologies, Springer, pp 23–32

  17. Polat K (2019) A hybrid approach to Parkinson disease classification using speech signal: the combination of SMOTE and random forests. In: 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), IEEE, pp 1–3

  18. Hartmann WM (2004) Dimension reduction vs. variable selection. In: International Workshop on Applied Parallel Computing, Springer, pp 931–938

  19. Ghazikhani A, Monsefi R, Yazdi HS (2013) Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams. Neural Comput Appl 23(5):1283–1295

    Article  Google Scholar 

  20. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AAJISPM (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65

    Article  Google Scholar 

  21. Jameel SK et al (2022) Exploiting the generative adversarial network approach to create a synthetic topography corneal image. Biomolecules 12(12):1888

    Article  Google Scholar 

  22. Kumar A et al. (2022) Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction. Multimed Tools Appl, 1–19

  23. Zhu B, Pan X, Vanden Broucke S, **ao J (2022) A GAN-based hybrid sampling method for imbalanced customer classification. Inf Sci 609:1397–1411

    Article  Google Scholar 

  24. Riordon J, McCallum C, Sinton D (2019) Deep learning for the classification of human sperm. Comput Biol Med 111:103342

    Article  Google Scholar 

  25. Javadi S, Mirroshandel SA (2019) A novel deep learning method for automatic assessment of human sperm images. Comput Biol Med 109:182–194

    Article  Google Scholar 

  26. Ilhan HO, Sigirci IO, Serbes G, Aydin N (2020) A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods. Med Biol Eng Comput 58(5):1047–1068

    Article  Google Scholar 

  27. Yibre AM, Koçer B (2021) Semen quality predictive model using Feed Forwarded Neural Network trained by Learning-Based Artificial Algae Algorithm. Eng Sci Technol Int J 24(2):310–318

    Google Scholar 

  28. Valiuškaitė V, Raudonis V, Maskeliūnas R, Damaševičius R, Krilavičius TJS (2021) Deep learning based evaluation of spermatozoid motility for artificial insemination. Sensors 21(1):72

    Article  Google Scholar 

  29. Ni T, Ding Y, Xue J, **a K, Gu X, Jiang Y (2021) Local constraint and label embedding multi-layer dictionary learning for sperm head classification. ACM Trans Multimed Comput Commun Appl (TOMM) 17(3):1–16

    Google Scholar 

  30. M. Yüzkat, H. O. Ilhan, N. J. C. i. B. Aydin, and Medicine, "Multi-model CNN fusion for sperm morphology analysis," Computers in Biology and Medicine, vol. 137, p. 104790, 2021.

  31. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):1–48

    Article  Google Scholar 

  32. Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A (2021) A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 65(5):545–563

    Article  Google Scholar 

  33. Human Sperm Head Morphology dataset (HuSHeM) [Online] Available: https://data.mendeley.com/datasets/tt3yj2pf38/3

  34. Wang K, Gou C, Duan Y, Lin Y, Zheng X, Wang F-Y (2017) Generative adversarial networks: introduction and outlook. IEEE/CAA J Automatica Sinica 4(4):588–598

    Article  MathSciNet  Google Scholar 

  35. Mirza M, Osindero S (2014) Conditional generative adversarial nets. ar**v preprint ar**v

  36. Patrick MK, Adekoya AF, Mighty AA, Edward BY (2019) Capsule networks–a survey. J King Saud University-Comput Inf Sci 34:1295–1310

    Google Scholar 

  37. Vijayakumar T (2019) Comparative study of capsule neural network in various applications. J Artif Intell 1(01):19–27

    Google Scholar 

  38. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. ar**v preprint ar**v

  39. Sun G, Ding S, Sun T, Zhang C (2021) SA-CapsGAN: using capsule networks with embedded self-attention for generative adversarial network. Neurocomputing 423:399–406

    Article  Google Scholar 

  40. Chang V et al (2014) Gold-standard and improved framework for sperm head segmentation. Comput Methods Programs Biomed 117(2):225–237

    Article  Google Scholar 

  41. Hoogi A, Wilcox B, Gupta Y, Rubin DL (2019) Self-attention capsule networks for object classification. ar**v preprint ar**v:1904.12483

  42. F Jafarzadehpour, AS Molahosseini, AAE Zarandi, L Sousa, (2019) Efficient modular adder designs based on thermometer and one-hot coding. IEEE transactions on very large scale integration (VLSI) systems, 27(9):2142–2155

  43. Mudeng V, Kim M, Choe S-W (2022) Prospects of structural similarity index for medical image analysis. Appl Sci 12(8):3754

    Article  Google Scholar 

  44. Nunn EJ, Khadivi P, Samavi S (2021) Compound frechet inception distance for quality assessment of gan created images. ar**v preprint ar**v:2106.08575

  45. Jeni LA, Cohn JF, De La Torre F (2013) Facing imbalanced data--recommendations for the use of performance metrics. In: 2013 Humaine association conference on affective computing and intelligent interaction, IEEE, pp 245–251

  46. Ali-Gombe A, Elyan E (2019) MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing 361:212–221

    Article  Google Scholar 

  47. Xu J, Zhang Y, Miao D (2020) Three-way confusion matrix for classification: A measure driven view. Inf Sci 507:772–794

    Article  MathSciNet  MATH  Google Scholar 

  48. Porcu S, Floris A, Atzori LJE (2020) Evaluation of data augmentation techniques for facial expression recognition systems. Electronics 9(11):1892

    Article  Google Scholar 

  49. Takahashi R, Matsubara T, Uehara K (2019) Data augmentation using random image crop** and patching for deep CNNs. IEEE Trans Circuits Syst Video Technol 30(9):2917–2931

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Control Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran

    Hamed Jabbari & Nooshin Bigdeli

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  1. Hamed Jabbari
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  2. Nooshin Bigdeli
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Jabbari, H., Bigdeli, N. New conditional generative adversarial capsule network for imbalanced classification of human sperm head images. Neural Comput & Applic 35, 19919–19934 (2023). https://doi.org/10.1007/s00521-023-08742-3

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