Ensembled Deep Learning for the Classification of Human Sperm Head Morphology

Abstract

Infertility is a growing global health concern, with male factor infertility contributing to half of all cases. Semen analysis is crucial to infertility diagnostics. However, sperm morphology assessment, as a routine part of analysis, is still performed manually and is thus highly subjective. Here, a stacked ensemble of convolutional neural networks (CNNs) is presented for automated classification of human sperm head morphology. By combining traditional CNN models with modern residual and densely connected architectures using a multi‐class meta‐classifier, classification rate improvements of 2.7% (to 98.2%) and 2.3% (to 63.3%) on the HuSHeM and SCIAN‐MorphoSpermGS (SCIAN) datasets, respectively, are achieved. This considerable improvement in prediction performance is achieved as the meta‐classifier improves upon the individual classification rates of the base models by ≈8.5%. The ensembled deep learning model is a powerful step toward an automated sperm morphology analysis, providing new opportunities to standardize clinical practice and reduce treatment costs to improve patient treatment.

Keywords

• computer vision
• deep learning
• meta learning
• sperm analysis
• sperm morphology