Comparison with state-of-the-art methods on sperm morphology classification between 2019 and 2025.

Comparison with state-of-the-art methods on sperm morphology classification between 2019 and 2025.

<p>The proposed framework outperforms all prior approaches in terms of classification accuracy.</p>

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Bibliographic Details
Main Author:	Şafak Kılıç (22227019) (author)
Published:	2025
Subjects:	Biotechnology Space Science Information Systems not elsewhere classified two benchmark datasets sophisticated feature engineering reproductive health evaluation reduced fertility rates random forest importance nearest neighbors algorithms deep feature engineering clinical implications include assisted reproductive procedures abnormal sperm morphology 77 ± 0 30 &# 8211 representing significant improvements sperm morphology analysis baseline cnn performance cam attention visualization pca ), chi significant time savings cbam attention mechanisms 08 ± 1 significant inter time analysis art performance xlink "> variance thresholding using 5 treatment outcomes test accuracies studies reporting strongly correlated square test rigorously evaluated resnet50 architecture research presents research demonstrates observer variability mcnemar ’ linear kernels fold cross expert evaluators existing state critical component best configuration art approaches 45 minutes 3000 images 216 images
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