Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
Source abstract
Abstract
Infertility is a global problem that affects many couples. Sperm analysis plays an essential role in the clinical diagnosis of human fertility. The examination of sperm morphology is an essential technique because sperm morphology is a proven indicator of biological functions. At present, the morphological classification of human sperm is conducted manually by medical experts. However, manual classification is laborious and highly dependent on the experience and capability of clinicians. To address these limitations, we propose a transfer learning method based on AlexNet to automatically classify the sperms into four different categories in terms of the World Health Organization (WHO) standards by analyzing their morphology. We adopt the feature extraction architecture of AlexNet as well as its pre-training parameters. Besides, we redesign the classification network by adding the Batch Normalization layers to improve the performance. The proposed method achieves an average accuracy of 96.0% and an average precision of 96.4% in the freely-available HuSHeM dataset, which exceeds the performance of previous algorithms. Our method shows that automatic sperm classification has great potential to replace manual sperm classification in the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sperm and Testicular Function · Reproductive Biology and Fertility · Spectroscopy Techniques in Biomedical and Chemical Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR 999077, China
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR 999077, China
Funding
Research Grants Council, University Grants Committee · National Natural Science Foundation of China-Guangdong Joint Fund
References (34)
Show source-provided references
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