Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms. In the segmentation step, clustering techniques with/without group sparsity approach were tested to extract region of interests from the images. Subsequently, a novel publicly available morphological sperm image data set, whose labels were identified by experts as non-sperm, normal and abnormal sperm, was created as the ground truths of classification step. In the classification step, conventional and ensemble machine learning methods were applied to domain-specific features that were extracted by using wavelet transform and descriptors. Additionally, as an alternative to conventional features, three deep neural network architectures, which can extract high-level features from raw images after using statistical learning, were employed to increase the proposed method's performance. The results show that, for the conventional features, the highest classification accuracies were achieved as 80.5% and 83.8% by using the wavelet- and descriptor-based features that were fed to the Support Vector Machines respectively. On the other hand, the Mobile-Net, which is a very convenient network for smartphones, achieved 87% accuracy. In the light of obtained results, it is seen that a fully automatic hybrid system, which uses the group sparsity to enhance segmentation performance and the Mobile-Net to obtain high-level robust features, can be an effective mobile solution for the sperm morphology analysis problem. A fully automated hybrid human sperm detection and classification system based on mobile-net.

中文翻译：

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基于移动网络的全自动混合型人类精子检测和分类系统，性能与常规方法比较。

精子形态作为生育能力的指标，是精液分析中的关键工具。在这项研究中，引入了基于智能手机的混合系统，该系统可完全自动化精子形态分析，以消除不必要的人为因素。拟议的混合系统包括两个逐步的步骤：可能的精子形状的自动分割和正常/非正常精子的分类。在分割步骤中，对使用/不使用组稀疏性方法的聚类技术进行了测试，以从图像中提取感兴趣区域。随后，创建了一个新的公开可用的形态学精子图像数据集，其标签被专家识别为非精子，正常精子和异常精子，以此作为分类步骤的基础。在分类步骤中，传统的和集成的机器学习方法应用于通过使用小波变换和描述符提取的领域特定特征。此外，作为常规功能的替代方法，采用了三种深度神经网络体系结构，可以在使用统计学习后从原始图像中提取高级特征，以提高所提出方法的性能。结果表明，对于常规特征，通过分别使用基于小波和描述符的特征分别输入到支持向量机，可以实现最高的分类准确率，分别为80.5％和83.8％。另一方面，对于智能手机而言，Mobile-Net是非常方便的网络，其准确性达到了87％。根据获得的结果，可以看到全自动混合动力系统 利用群体稀疏性提高分割性能，并使用Mobile-Net获得高级鲁棒性功能，可以成为解决精子形态分析问题的有效移动解决方案。基于移动网络的全自动混合型人类精子检测和分类系统。