Identification of Abnormal Spermatozoa Motility Using the SVM Algorithm

Mohammad Daniel Sulthonul Karim; Eva Yulia Puspaningrum; I Gede Susrama Mas Diyasa

doi:10.56480/jln.v5i1.1324

Authors

Mohammad Daniel Sulthonul Karim Informatics Study Program, Faculty of Computer Science, Pembangunan Nasional “Veteran” University, East Java, Surabaya
Eva Yulia Puspaningrum Informatics Study Program, Faculty of Computer Science, Pembangunan Nasional “Veteran” University, East Java, Surabaya
I Gede Susrama Mas Diyasa Informatics Study Program, Faculty of Computer Science, Pembangunan Nasional “Veteran” University, East Java, Surabaya

DOI:

https://doi.org/10.56480/jln.v5i1.1324

Abstract View:

21

PDF downloads:

4

Keywords:

Spermatozoa, Motility, TrackPy, SVM

Abstract

Spermatozoa motility is one of the key indicators in determining male fertility quality. Manual assessment of motility abnormalities often requires significant time and effort, thus necessitating a more efficient and accurate automated approach. This study aims to identify abnormalities in spermatozoa motility using the Support Vector Machine (SVM) algorithm, utilizing microscopic video data analyzed through TrackPy for spermatozoa trajectory tracking. The analysis process involves data acquisition, spermatozoa detection in each frame, sperm trajectory construction, and trajectory classification into normal or abnormal categories. The SVM model was trained using a dataset derived from spermatozoa trajectories classified based on parameters such as average velocity and trajectory linearity. The results show that the method achieved the highest accuracy of 89 percent in identifying spermatozoa motility abnormalities in HD resolution videos with a frame rate of 30 fps.

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