# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 1 Introduction  
Infertility is a worldwide problem that affects more than 10% of couples, among which about 30% to 50% are found to be related to men [1]. Sperm quality is the most important criterion for measuring male reproductive ability. The quality of human sperm not only affects the probability of conception but also profoundly impacts the physical and mental health of offspring. Sperm analysis is an essential step to identify sperm quality in clinical diagnosis. Generally, analysis of sperm cells includes sperm motility detection and sperm morphology examination. Sperm morphology is a proven indicator of male fertility [2], and morphology assessment classifies the sperm in multiple categories [3, 4]. Previous studies have shown that teratozospermia (i.e., the increased concentration of abnormal sperms) is affected by various factors, such as aging and genetics [5, 6]. Therefore, morphological classification of sperm is helpful to promote pathological research in human reproduction. Besides, in vitro fertilization also needs to find sperm with excellent quality quickly, which further requires the rapid and accurate classification of sperm. Human sperm consists of three main parts: head, midpiece and tail (Figure 1a) [7]. According to the World Health Organization, human sperm can be divided into normal sperm and abnormal sperm, and the abnormal sperm can be further classified into four sub-categories: head defects, neck and midpiece defects, tail defects, and excess residualcytoplasm [8]. Among these defects, head defects, including tapered, pyriform, no acro-some, small, amorphous, vacuolated and small acrosomal areas, have the most significant impact on sperm quality [8]. Herein, we mainly focus on the head malformations in this research. Although the Computer-Assisted Semen Analysis (CASA) system has been applied in various scenarios for semen analysis, it has not yet been able to classify human sperm morphologically [9]. In clinical applications, human sperm morphology analysis is conducted manually by experienced physicians, which is laborious, time-consuming, and highly subjective [10; 11]. To address the limitations of manual human spermatazoon classification, researchers have recently developed various automatic sperm morphology classifiers. According to WHO criteria, the four classes of sperm heads, namely tapered, pyriform, amorphous, and normal sperm (as shown in Figure 1b), are almost indistinguishable by embryologists because they have limited differences. Therefore, the classification algorithms are mainly focused on the classification of these four categories. The earliest algorithms for sperm head classification were based on traditional machine learning methods. In 2017, [PERSON] et al. introduced a gold-standard tool (SCIAN-MorphoSpermGS dataset) to evaluate and compare the classification approaches for human sperm heads [12]. The SCIAN dataset consists of 1854 sperm cell images and includes five different expert-classification categories: normal, tapered, pyriform, small, and amorphous. Subsequently, they proposed a two-stage algorithm to classify sperm heads into the five aforementioned classes of the SCIAN dataset and achieved an average classification accuracy of 58% [13]. In the first stage, the amorphous sperm cells are filtered out, while the remaining four classes are also preliminarily classified. The second stage works as a verifier to verify the results of the previous stage. In another study, [PERSON] et al. employed an adaptive patch-based dictionary learning (APDL) approach to improve the average true positive rate to be 62% on the SCIAN dataset and achieve an average true positive rate of 92.3% on the HuSHeM dataset for automatic sperm head classification [14]. In the APDL scheme, small square patches are acquired from the sample sperm head images to train the  
Figure 1: Sperm morphology: (**a**) Different parts of a sperm. (**b**) Four different categories of sperm in the HuSHeM dataset compared with the WHO schematic drawings.