Automatic Assessment of Human Sperm Images with Capsule Networks

Infertility which is a psychologically threatening and emotionally stressful problem is seen approximately 15% of couples in worldwide. Recent studies have shown that in 40-50% of couples evaluated for infertility, the problem is caused by the male individual. Sperm morphology analysis that provides separation of normal and abnormal sperm is very important in evaluating male infertility and showing the causes. Since manual evaluation of sperm morphology is time consuming and subjective, automatic assessment methods are needed. In this study, Capsule Networks, a special model of Deep Neural Networks (DNN), are used for the classification of human sperm head images. The classification performances of capsule networks are measured using the Modified Human Sperm Morphology Analysis dataset (MHSMA). The results show that the best classification accuracy is achieved as 73%.

Keywords:

Capsule networks infertility, sperm morphology,

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