Researchers have developed an attention-guided deep learning framework to improve the interpretability and accuracy of sperm morphology classification. By integrating a pre-trained EfficientNet-B0 model with a Convolutional Block Attention Module (CBAM), the system effectively focuses on critical sperm head features. This approach achieved high accuracy rates of 90.2% and 93.9% on public datasets, surpassing simpler models and providing visual explanations for its classifications. AI
IMPACT This research offers a more transparent and accurate AI tool for clinical applications in fertility analysis.
RANK_REASON The cluster contains an academic paper detailing a new deep learning framework for a specific classification task.
- Convolutional Block Attention Module
- EfficientNet B0
- Grad-CAM++
- SimpleCNN
- Smids
- Zahra Asghari Varzaneh
- arXiv
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