Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 111, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2019.103342
Keywords
Convolutional neural network; Deep learning; Fertility; Sperm selection; Sperm diagnostics; Sperm head classification; Transfer learning
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Funding
- Natural Sciences and Engineering Council of Canada [CHRP 508388-17]
- Canadian Institutes of Health Research [CPG 50838817]
- Discovery Accelerator program [477898-2015-RGPAS]
- Steacie Memorial Fellowship [492246-2016]
- Canada Research Chairs program [230931]
- Discovery Grants program [RGPIN-2015-06701]
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Background: Infertility is a global health concern, and couples are increasingly seeking medical assistance to achieve reproduction. Semen analysis is a primary assessment performed by a clinician, in which the morphology of the sperm population is evaluated. Machine learning algorithms that automate, standardize, and expedite sperm classification are the subject of ongoing research. Method: We demonstrate a deep learning method to classify sperm into one of several World Health Organization (WHO) shape-based categories. Our method uses VGG16, a deep convolutional neural network (CNN) initially trained on ImageNet, a collection of human-annotated everyday images, which we retrain for sperm classification using two freely-available sperm head datasets (HuSHeM and SCIAN). Results: Our deep learning approach classifies sperm at high accuracy and performs well in head-to-head comparisons with earlier approaches using identical datasets. We demonstrate improvement in true positive rate over a classifier approach based on a cascade ensemble of support vector machines (CE-SVM) and show similar true positive rates as compared to an adaptive patch-based dictionary learning (APDL) method. Retraining an off-the-shelf VGG16 network avoids excessive neural network computation or having to learn and use the massive dictionaries required for sparse representation, both of which can be computationally expensive. Conclusions: We show that our deep learning approach to sperm head classification represents a viable method to automate, standardize, and accelerate semen analysis. Our approach highlights the potential of artificial intelligence technologies to eventually exceed human experts in terms of accuracy, reliability, and throughput.
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