sperm morphology

Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning

A study proposes an interpretable deep learning framework combining EfficientNet-B0 with a Convolutional Block Attention Module for sperm morphology classification, achieving 90.2% and 93.9% accuracy on SMIDS and HuSHem datasets respectively.