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MFDF-UNet: Multiscale feature depth-enhanced fusion network for colony adhesion image segmentation
Liu Hui1, Wang Zhiyi1, Li Xue2
1School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China.
Journal of Microbiological Methods
|June 26, 2025
Summary
A new deep learning model, MFDF-UNet, significantly improves colony adhesion image segmentation for food safety. It achieves higher accuracy and mean intersection-over-union (mIoU) compared to existing methods, enhancing food quality assessments.
Area of Science:
- Computer Vision
- Food Science
- Machine Learning
Background:
- Colony counting is vital for food quality and safety evaluations.
- Accurate segmentation of colony adhesion images is essential for precise food safety assessments.
Purpose of the Study:
- To introduce a novel deep learning network, MFDF-UNet, for high-precision colony adhesion image segmentation.
- To enhance the extraction, integration, and transfer of multi-scale features for improved segmentation accuracy.
Main Methods:
- Developed the MFDF-UNet with a self-similar fusion fractal structure for recursive layer integration.
- Incorporated depth-enhanced connectivity (DEC) units and progressive fusion (PF) modules to accumulate detailed features.
- Strengthened inter-layer information transfer to maintain feature consistency and address information imbalance.
Main Results:
- MFDF-UNet achieved 77.95% segmentation accuracy, 97.55% precision, and 57.94% mIoU on the AGAR-based hybrid colony adhesion dataset.
- Outperformed leading deep learning methods, including ResUNet, by significant margins in accuracy and mIoU.
- Demonstrated superior performance despite requiring more parameters and training time.
Conclusions:
- MFDF-UNet offers a significant advancement in colony adhesion image segmentation.
- The model's enhanced feature extraction and fusion capabilities lead to improved accuracy and reliability in food safety applications.
- The performance gains justify the computational cost, highlighting its potential for practical implementation.
