# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 1 Introduction  
Infertility is a widespread health concern, affecting about 15% of couples globally, with male-related factors responsible for 30-40% of cases. Abnormal sperm head morphology is one of the main factors contributing to this problem [1]. A typical sperm head is oval and consists primarily of the acrosome and nucleus. Upon reaching the egg, the acrosome releases hydrolytic enzymes that degrade the protective layers, corona radiata, and zona pellucida, allowing the sperm's genetic material to enter and initiate fertilization [2]. Abnormal size, shape, or structure of the sperm head can impair motility and compromise its ability to penetrate protective barriers, reducing fertilization potential [3]. Accurate sperm morphology analysis is essential for fertility prognosis and treatment planning. There exists a wide range of abnormal sperm head morphologies that can impair fertility and reduce the likelihood of successful fertilization. Among these, amorphous, pyriform, and tapered forms are the most frequently observed [4], as illustrated in Figure 1. Amorphous heads lack symmetry and defined structure, with irregular borders deviating from the standard oval shape. Pyriform heads display a narrowing from base to tip, resembling a pear shape; although they maintain symmetry along the long axis, the short axis shows pronounced asymmetry. Tapered heads are excessively elongated, often ending in a sharp or pointed tip, diverging significantly from the standard oval structure. These abnormal morphologies can impair sperm motility, acrosome function, and DNA integrity, ultimately reducing fertility potential and decreasing the likelihood of successful fertilization. Traditionally, sperm morphology has been manually evaluated by trained clinicians using microscopy. The assessment involves preparing specimens by smearing, washing, and staining semen samples. Under the microscope, at least 200 sperm heads and tails are examined for morphological features [3]. The proportion of normal sperm is then calculated to determine whether it meets clinical criteria. However, this method is labor-intensive, highly subjective, and prone to inconsistencies across different laboratories and observers [6; 7]; inter-laboratory coefficients of variation in analysis results have been reported to range from 4.8% to as high as 132% [8]. Computer-aided sperm analysis (CASA) systems have been developed to automate and standardize sperm morphology evaluation to address these limitations, reducing subjectivity and improving consistency. In CASA systems, sperm head morphology recognition algorithms based on digital image processing techniques form a crucial element. Traditional algorithms have largely relied on hand-crafted features derived from images, including commonly used features such as area, length-to-width ratio, perimeter, Fourier descriptors, image moments, and image gradient [9; 10]. Some studies design specialized ones targeting the symmetry of abnormal sperm heads. For example, when sperm heads are oriented horizontally, pyriform sperm exhibits symmetry along the long axis but asymmetry along the short axis (Figure 2), which can be captured using features like Quadrant Fitness and Bilateral Symmetry [4]. However, handcrafted feature extraction involves multiple steps, including image preprocessing, feature extraction, sperm head segmentation, and numerical analysis, making the algorithms overly complex with numerous hyperparameters. This complexity can lead to cumulative errors and reduced efficiency. In recent years, research on sperm morphology analysis has increasingly focused on deep learning models, which offer the advantage of automatically learning key features from images without the need for manual feature extraction. For example, VGG16 achieved an accuracy of 94% for identifying tapered, pyriform, amorphous, and small-headed sperm on the HuSHeM dataset [5; 11; 12]. InceptionV3 reached an accuracy of 87.3% [13; 14], and other models without significant modifications also achieved over 85% accuracy [15].