Two-Stage Probabilistic Classification of Sperm Identity and Morphology

Background/Objectives: From a histological and embryological perspective, sperm identification and morphological classification are distinct assessment tasks. This study compares image-level error distributions and review-referral outcomes between separately trained conditional components and direct three-class learning.

Methods

Following duplicate-image and label audits, 2947 images from the Sperm Morphology Image Dataset (SMIDS) were analyzed without changing the source labels. Logistic regression models using frozen EfficientNet-B0 features were evaluated using five outer and three inner cross-validation folds that preserved 2942 similarity groups. Final class probabilities, component probabilities, and outcomes at matched review volumes were compared.