Background and objectives

Semen analysis is a primary and mandatory procedure to evaluate the infertility during clinical examination. This procedure includes the analysis and classification of normal and abnormal Sperm, selection and efficient tracking of healthy sperm in the sample. Many methods were proposed earlier for the analysis of semen. The fast sperm movement and high dense cluster of sperm is a challenging task for researchers.

Methods

The paper proposes a novel Faster Region Convolutional Neural Network (FRCNN) with Elliptic Scanning Algorithm (ESA) for classifying human sperm and a Novel Tail to Head movement algorithm (THMA) for the motility analysis and tracking. This proposed method improves the accuracy of computer assisted semen analysis (CASA).

Results

The proposed method outperforms and provides better results than existing methods. Method provides better accuracy of 97.37%. Sperm detection and identifying the sperm motility in the group is performed with minimum execution time of 1.12 s.

Conclusions

A novel FRCNN with ESA detection algorithm is proposed for the analysis of human sperm classification. This method provides an accuracy of 97.37%. A Tail head movement-based (THMA) algorithm is explained for the motility analysis.

中文翻译：

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用于精子分析的更快区域卷积神经网络和精液跟踪算法

背景和目标

精液分析是临床检查期间评估不孕症的主要和强制性程序。该程序包括对正常和异常精子的分析和分类，样本中健康精子的选择和有效跟踪。早先提出了许多用于分析精液的方法。快速的精子运动和高密度的精子簇对研究人员来说是一项具有挑战性的任务。

方法

该论文提出了一种新的具有椭圆扫描算法 (ESA) 的更快区域卷积神经网络 (FRCNN) 用于对人类精子进行分类，并提出了一种用于运动分析和跟踪的新型尾对头运动算法 (THMA)。这种提议的方法提高了计算机辅助精液分析 (CASA) 的准确性。

结果

所提出的方法优于现有方法并提供了更好的结果。方法提供了更好的准确度 97.37%。以 1.12 秒的最短执行时间执行精子检测和识别组中的精子活力。

结论

提出了一种具有 ESA 检测算法的新型 FRCNN，用于分析人类精子分类。该方法提供了 97.37% 的准确率。为运动分析解释了基于尾头运动 (THMA) 的算法。