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Non-destructive Pre-incubation Egg Fertilization Detection Using Low-field MRI and Efficientnetv2
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Abstract
To address the challenge of non-destructive assessment of pre-incubation egg fertilization status, this study proposes an early screening method for infertile eggs combining low-field MRI with deep learning. A rapid spin echo (FSE) acquisition protocol was developed on a 0.3 T MRI system to obtain 3,200 pre-incubation egg images. The pre-trained EfficientNetV2 backbone network was enhanced with Spatial Enhancement (SE) attention and Fused MBConv modules, employing progressive input scaling and regularization scheduling for end-to-end training. GradCAM validation confirmed that the model’s discrimination of key regions like the blastodisc and yolk aligns with MRI contrast mechanisms. Experimental results demonstrated 98.37% accuracy, 98.32% F1 score, and approximately 14 FPS inference speed. Thermal maps revealed the model’s focus on high-signal annular regions around the blastodisc, consistent with pre-incubation morphology. Compared to light imaging, ultrasound, and spectral methods, low-field MRI shows stable imaging of eggshell color/thickness and internal microstructures. This study establishes a practical new paradigm for non-destructive fertilization assessment of hatching eggs, while providing insights for addressing industry challenges such as early sex determination in the hatching sector.
Keywords
Magnetic resonance imaging; low-field MRI; EfficientNetV2; SE attention; Fused-MBConv; Grad-CAM
References
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How to cite this paper
Non-destructive Pre-incubation Egg Fertilization Detection Using Low-field MRI and Efficientnetv2
How to cite this paper: Tanyu Lin, Kaiwen Huang, Shuyu Cheng, Zhuoheng Tang, Zhoucai Ou, Jiye Zeng, Yuanyang Mao. (2025) Non-destructive Pre-incubation Egg Fertilization Detection Using Low-field MRI and Efficientnetv2. Advances in Computer and Communication, 6(5), 274-279.
DOI: http://dx.doi.org/10.26855/acc.2025.12.003
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