Deep learning-based sperm image analysis to support assessment of male reproductive health

Nowadays, male infertility is a worldwide issue. This problem can be caused by various factors such as low sperm count, weak sperm, anti-sperm antibodies, blocked sperm ducts, congenital infertility, and so on. In fact, if the problem can be detected early, it could be completely solved in some specific cases. This paper proposes a new, more comprehensive framework based on a deep learning model that can address two phases including detection and classification, which are the main steps in solving infertility problems. Experiments conducted on some datasets show that our proposed model has achieved high efficiency compared to other models.