# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 1 Introduction
With the ongoing development of deep learning, various new techniques have been introduced. Figure 1: The typical abnormal sperm head morphology [5]. Figure 2: The symmetry of normal, pyriform, and amorphous sperm heads. For example, a combination of Generative Adversarial Networks (GANs) and Capsule Networks (CapsNets) has been employed to synthesize sperm images, effectively addressing data imbalance issues and achieving an accuracy of 97.8% [16; 17]. Similarly, SHMC-Net, a model designed for both sperm segmentation and classification, integrates features across multiple scales and has achieved a notable accuracy of 98.3% [18]. These models have demonstrated high accuracy when trained on specific datasets, surpassing traditional methods in terms of precision and robustness. Classical ensemble methods remain effective for sperm head classification. For example, integrating SHMC-Net models with different structural variations achieved an accuracy of 99.17%. Similarly, some studies have combined Transformer and MobileNet [19] or integrated VGG16, VGG19, ResNet34, and DenseNet161 [20], with accuracy surpassing that of individual models. However, the computational complexity of these models is considerable, which limits their feasibility for practical clinical applications. However, deep learning models are sensitive to changes in the target position and orientation, which affects their robustness and limits their applicability in diverse clinical settings [21; 22]. Moreover, the sperm head under examination often overlaps with other sperm heads or noise, causing the network to extract irrelevant features, which interfere with predictions. Most studies manually standardize the position, angle, and scale of the sperm head before inputting it into the model for classification [12; 23]. This approach is cumbersome and labor-intensive, and it is also influenced by human subjectivity and fatigue, making it difficult to improve the accuracy and efficiency of sperm head morphology classification in clinical settings. In this paper, we propose an adaptive sperm head pose correction and morphology classification model. We use EdgeSAM for initial feature extraction and segmentation, suppressing irrelevant content in the feature map through prompts [24]. Then, a sperm head poses a correction network, and Rotated RoI alignment is used to normalize the sperm head's position and orientation [25]. To leverage the symmetry of pyriform and amorphous sperm heads in morphology classification, we adapted a flip feature fusion module with deformable convolutions to align and enhance feature maps, ultimately improving classification accuracy. Lastly, this architecture has been rigorously tested across two sperm head image datasets, demonstrating its effectiveness and superiority. The main contributions of this article are summarized as follows:  
1. We introduce EdgeSAM into the sperm head segmentation task, using a single coordinate point as a prompt to indicate the rough location of the sperm head, enabling accurate feature extraction and segmentation for the specific sperm. 2. We propose a sperm head pose correction network that can accurately predict the position, angle, and orientation of the sperm head, achieving standardization with a low computational cost. This significantly improves the accuracy and efficiency of sperm head morphology classification. 3. We propose a flip feature fusion module that leverages the symmetry of pyriform and amorphous sperm heads by processing flipped feature maps to enhance the accuracy of sperm head morphology classification.