<![CDATA[Infertility remains a major challenge in global reproductive healthcare, with abnormal sperm motility being one of the primary contributing factors to male infertility. Conventional sperm motility analysis relies heavily on manual microscopic observation, which is subjective, time-consuming, and prone to inter-operator variability. Although Computer-Aided Sperm Analysis (CASA) systems provide automated solutions, their high cost and limited accessibility restrict widespread clinical adoption. Therefore, there is a need for a lightweight and cost-effective computational approach that can automatically classify sperm motility abnormalities with high accuracy. This study proposes a machine learning framework based on a Multilayer Perceptron (MLP) neural network optimized using adaptive optimization algorithms to classify sperm motility trajectories. The model utilizes two kinematic features extracted from microscopic video tracking: mean velocity and trajectory linearity. A dataset consisting of 276 unique sperm trajectories was obtained from microscopic recordings and processed using the Trackpy library to generate motion trajectories. The dataset was divided using an 80:20 train–test split strategy to evaluate the generalization capability of the model. Four adaptive optimization algorithms were systematically evaluated, including Adagrad, Adadelta, FTRL, and Adamax. The neural network was trained for 50 epochs using binary cross-entropy as the loss function and evaluated using accuracy, per-class precision, recall, F1-score, specificity, balanced accuracy, and ROC-AUC. Experimental results demonstrate that the Adamax optimizer achieved the best performance with an accuracy of 96.43% and a perfect ROC-AUC of 1.00, outperforming Adagrad (92.86%), FTRL (69.64%), and Adadelta (67.86%). The results indicate that Adamax provides more stable convergence and better optimization behavior for dense kinematic feature spaces. These findings highlight the effectiveness of adaptive optimization techniques in improving neural network performance for automated sperm motility classification. The proposed approach offers a computationally efficient and accessible alternative for supporting clinical sperm analysis and reducing subjectivity in reproductive diagnostics]]>
