MADRNet: Morphology-Aware Dual-Path Reversible Network for Sperm Classification

Sperm morphology analysis plays a crucial role in the clinical diagnosis of male infertility. However, manual evaluation is inherently subjective, and inconsistencies in diagnostic criteria may compromise accuracy. Some existing sperm image classification models are introduced but requiring manual intervention. Most models lack of consideration of alignment between computational classification and WHO sperm morphology standards. To address these challenges, we propose an innovative morphology-aware dual-path reversible network (MADRNet) in designing our model. We integrate key biomarkers, such as head aspect ratio and acrosomal integrity, both of which are crucial for clinical sperm assessment, into the network. Particularly, the network utilizes a dual-path attention mechanism, incorporating both parallel spatial and channel attention, while embedding the acrosome anatomical constraint within the channel attention. To further enhance the alignment of our model with the WHO standards, we develop a dynamic loss function considering head aspect ratio constraint. Further, we employ a reversible architecture to enable the model to preserve more microscopic details while reducing GPU memory consumption. Experiments on the HuSHeM dataset demonstrate that the model achieves an accuracy of 96.3% and an F1 score of 96.8%. Meanwhile, the model maintains a real-time processing speed of 32ms per image, providing a precise and efficient solution for clinical sperm screening. The implementation source code and the underlying dataset are available at https://github.com/fanyangZK/MADRNet.