Loss-Based Ensemble Generative Adversarial Network Model for Enhancing the Sperm Morphology Classification

Abstract

Infertility has emerged as a significant health issue impacting individuals' lives. In prior investigations, image classification has been applied to identify morphologic abnormalities associated with infertility issues. However, the limited data availability has impeded high performance. In the field of image augmentation techniques, particularly concerning generative adversarial networks (GANs), an alternative approach can encounter a significant issue known as mode collapse. This phenomenon arises when the generator consistently produces a restricted set of identical or highly similar images, which may negatively affect the overall performance and accuracy of the model. Consequently, the aim of this study is to mitigate mode collapse by employing loss-based ensemble GAN framework, formulated based on the integration of two distinct GAN models. In addition, a comprehensive analysis is carried out using an expanded approach involving three GAN models in conjunction with a spatial augmentation technique. The Shifted Window Transformer model achieves 95.37% accuracy on the HuSHeM dataset, outperforming other classification models. This finding shows enhanced accuracy relative to earlier studies using the identical dataset.