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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

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Automated chick gender determination using optical coherence tomography and deep learning.

Jadsada Saetiew¹, Papawit Nongkhunsan¹, Jiraporn Saenjae¹

¹School of Physics, Institute of Science, Suranaree University of Technology, 111 University Avenue, Muang, Nakhon ratchasima 30000, Thailand.

Poultry Science

|March 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel, non-invasive chick sexing method using Optical Coherence Tomography (OCT) and deep learning. The custom model achieved 79% accuracy, offering a scalable alternative to traditional, labor-intensive techniques.

Keywords:

Chick gender classification Deep learning Machine learning Optical coherence tomography Vent sexing

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Area of Science:

Poultry Science
Biomedical Engineering
Machine Learning

Background:

Traditional chick sexing methods (vent sexing, ultrasound) are labor-intensive and require expert skills.
Existing techniques lack the resolution for precise, non-invasive gender determination.
Optimizing poultry production necessitates efficient and accurate chick sexing.

Purpose of the Study:

To develop a high-resolution, non-invasive chick sexing method using Optical Coherence Tomography (OCT) and deep learning.
To create a custom deep learning model optimized for OCT data to improve classification accuracy.
To establish a scalable, real-time alternative to expert-dependent manual sexing.

Main Methods:

Integration of Optical Coherence Tomography (OCT) for micrometer-scale visualization of internal cloacal structures.
Development of a custom Convolutional Neural Network (CNN) tailored for OCT data.
Implementation of asymmetric image resizing and enhanced feature extraction within the CNN architecture.

Main Results:

The custom CNN model achieved 79% accuracy in chick gender classification.
The developed model significantly outperformed standard architectures like Inception (63%) and VGG-16 (74%).
Demonstrated the feasibility of differentiating sexes based on internal anatomical markers visualized by OCT.

Conclusions:

The novel OCT and deep learning approach provides a precise, non-invasive method for automated chick sexing.
This technology offers a scalable and efficient alternative to traditional, labor-intensive sexing methods.
Potential to reduce reliance on skilled labor in commercial hatcheries and enhance overall poultry production efficiency.