Department of Computer Engineering, Yildiz Technical University (YTU), 34220, Istanbul, Turkey.
Faculty of Engineering and Information Technologies, School of Biomedical Engineering, University of Sydney, Sydney, Australia.
Med Biol Eng Comput. 2020 May;58(5):1047-1068. doi: 10.1007/s11517-019-02101-y. Epub 2020 Mar 6.
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.
精子形态作为生育能力的一个指标，是精液分析中一个重要的工具。在这项研究中，引入了一种基于智能手机的混合系统，该系统完全实现了精子形态分析的自动化，旨在消除不必要的人为因素。所提出的混合系统由两个渐进的步骤组成：可能的精子形状的自动分割和正常/异常精子的分类。在分割步骤中，测试了具有/不具有组稀疏方法的聚类技术，以从图像中提取感兴趣区域。随后，创建了一个新的公开的形态学精子图像数据集，其标签由专家确定为非精子、正常和异常精子，作为分类步骤的地面真相。在分类步骤中，传统和集成机器学习方法被应用于使用小波变换和描述符提取的特定于域的特征。此外，作为传统特征的替代，使用统计学习从原始图像中提取高级特征的三种深度神经网络架构被用于提高所提出的方法的性能。结果表明，对于传统特征，使用分别馈送到支持向量机的基于小波和基于描述符的特征，可以获得 80.5%和 83.8%的最高分类精度。另一方面，Mobile-Net，这是一种非常适合智能手机的便捷网络，达到了 87%的精度。根据所获得的结果，可以看出，一种完全自动化的混合系统，使用组稀疏来增强分割性能，并使用 Mobile-Net 获得高级稳健的特征，可以成为精子形态分析问题的有效移动解决方案。基于 Mobile-Net 的全自动混合人类精子检测和分类系统。
