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Sperm Structure and Semen Composition

Sperm Structure and Semen Composition

During ejaculation, males release around 2-5 milliliters of semen, which is a complex mixture of mature sperm and various fluids produced by accessory glands. The mature sperm cells measure approximately 60 micrometers in length and consist of a head, neck, midpiece, and tail. The head is flattened and tapered, measuring about 4 to 5 micrometers in length. It contains a nucleus with condensed chromosomes and an acrosome, a cap-like structure filled with enzymes essential for penetrating the...

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Updated: Sep 18, 2025

Fish Sperm Assessment Using Software and Cooling Devices

Fish Sperm Assessment Using Software and Cooling Devices

Published on: July 28, 2018

Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment.

Abdulsamet Aktas¹, Taha Cap², Gorkem Serbes³

¹Department of Computer Engineering, Faculty of Technology, Marmara University, 34840 Istanbul, Turkey.

Diagnostics (Basel, Switzerland)

|June 26, 2025

Summary

This study developed an automated system for classifying sperm morphology, improving male infertility diagnosis. The novel ensemble model achieved 67.70% accuracy, outperforming traditional methods.

Keywords:

Support Vector Machines combined decision mechanisms feature extraction penultimate layer classification sperm morphology

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Last Updated: Sep 18, 2025

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Published on: November 28, 2018

Area of Science:

Reproductive Medicine
Artificial Intelligence in Healthcare
Computational Biology

Background:

Sperm morphology is crucial for male infertility diagnosis and assisted reproductive technologies like IVF and ICSI.
Manual evaluation of sperm morphology is subjective and inconsistent, necessitating automated solutions.
Accurate sperm morphology assessment aids in reproductive healthcare decision-making.

Purpose of the Study:

To develop a robust, fully automated framework for classifying sperm morphology.
To minimize observer variability in sperm morphology assessment.
To improve diagnostic support in male fertility evaluation.

Main Methods:

An ensemble-based classification approach combining Convolutional Neural Network (CNN) features.
Feature-level and decision-level fusion techniques using EfficientNetV2 variants.
Classification via Support Vector Machines (SVM), Random Forest (RF), and Multi-Layer Perceptron with Attention (MLP-Attention), with soft voting for decision fusion.

Main Results:

The ensemble framework achieved 67.70% accuracy on the Hi-LabSpermMorpho dataset (18 classes).
The fusion-based model significantly outperformed individual classifiers.
Ensemble techniques and multi-CNN integration addressed class imbalance and improved generalizability.

Conclusions:

The proposed methodology offers a substantial improvement over traditional and single-model approaches for automated sperm morphology classification.
Ensemble learning and multi-level fusion provide a reliable and scalable solution for clinical male fertility assessment.
This automated system enhances diagnostic accuracy and consistency in reproductive healthcare.