Infertility has recently emerged as a severe medical problem. The essential elements in male infertility are sperm morphology, sperm motility, and sperm density. In order to analyze sperm motility, density, and morphology, laboratory experts do a semen analysis. However, it is simple to err when using a subjective interpretation based on laboratory observation. In this work, a computer-aided sperm count estimation approach is suggested to lessen the impact of experts in semen analysis. Object detection techniques concentrating on sperm motility estimate the number of active sperm in the semen. This study provides an overview of other techniques that we can compare. The Visem dataset from the Association for Computing Machinery was used to test the proposed strategy. We created a labelled dataset to prove that our network can detect sperms in images. The best not-super tuned result is mAP 72.15.

Faculty of Informatics and Management Center for Basic and Applied Research University of Hradec Kralove 500 03 Hradec Kralove Czech Republic

Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai 81310 Malaysia

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