Abstract

Accurate morphological classification of human sperm cells is critical in diagnosing male infertility and automating assisted reproductive technologies (ART). Deep learning-based solutions have demonstrated remarkable performance in this domain. However, the opaque nature of these models poses challenges in clinical acceptance. This study presents an Explainable AI (XAI) framework for the analysis of sperm morphology in reproductive health, using CNN architectures to classify and interpret morphological features of sperm cells. The study employs Grad-CAM (Gradient-weighted Class Activation Mapping) to provide visual explanations for the model's decision-making process, enabling enhanced interpretability of deep learning models in the biomedical domain. The framework focuses on key sperm morphological components, including the head, vacuole, tail, and acrosome, assessing the performance of VGG16, ResNet34 and DenseNet-121 across these categories. Through a comparative evaluation, this study demonstrates the performance of various CNN architectures and the effectiveness of Grad-CAM in highlighting important regions within sperm images, thus providing transparency into the classification process and ensuring model trustworthiness.

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