Detection of Abnormal Human Sperm Morphology Using Support Vector Machine (SVM) Classification

Abnormal sperm morphology is a key indicator of male infertility, making its accurate detection crucial for reproductive health assessments. This study explores the application of Support Vector Machine (SVM) classification to automatically detect abnormalities in human sperm morphology. A dataset of microscopic sperm images was collected and labelled based on normal and abnormal morphological features, including head shape, midpiece defects, and tail irregularities. Feature extraction techniques were employed to quantify key morphological characteristics, which were then used to train the SVM model. The proposed SVM-based approach demonstrated high accuracy in classifying normal versus abnormal sperm morphology, significantly reducing the time and error associated with manual analysis. This method provides an efficient, automated solution for andrology laboratories and fertility clinics, enhancing diagnostic consistency and reliability. By incorporating machine learning techniques, this system holds promise for improving the precision of sperm morphology analysis, ultimately contributing to better fertility treatments and outcomes