Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP)

2025-09-09T20:47:43+00:00

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.

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Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP)