Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm

Image segmentation is a crucial aspect of clinical decision making in medicine, and as such, it has greatly enhanced the sustainability of medical care. Consequently, biomedical image segmentation has become a prominent research area in the field of computer vision. With the advent of deep learning,...

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Vydáno v:Journal of personalized medicine Ročník 13; číslo 9; s. 1298
Hlavní autoři: Khouy, Mohammed, Jabrane, Younes, Ameur, Mustapha, Hajjam El Hassani, Amir
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 25.08.2023
MDPI
Témata:
Accuracy
Algorithms
Automation
Datasets
Decision making
Deep learning
Design
Genetic algorithms
Genetic research
Image processing
Literature reviews
Liver
Machine learning
Machine vision
Medical imaging
Medical imaging equipment
Medical research
Mutation
Neural networks
Optimization
Precision medicine
Semantics
Germany
ISSN:2075-4426, 2075-4426
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Shrnutí:Image segmentation is a crucial aspect of clinical decision making in medicine, and as such, it has greatly enhanced the sustainability of medical care. Consequently, biomedical image segmentation has become a prominent research area in the field of computer vision. With the advent of deep learning, many manual design-based methods have been proposed and have shown promising results in achieving state-of-the-art performance in biomedical image segmentation. However, these methods often require significant expert knowledge and have an enormous number of parameters, necessitating substantial computational resources. Thus, this paper proposes a new approach called GA-UNet, which employs genetic algorithms to automatically design a U-shape convolution neural network with good performance while minimizing the complexity of its architecture-based parameters, thereby addressing the above challenges. The proposed GA-UNet is evaluated on three datasets: lung image segmentation, cell nuclei segmentation in microscope images (DSB 2018), and liver image segmentation. Interestingly, our experimental results demonstrate that the proposed method achieves competitive performance with a smaller architecture and fewer parameters than the original U-Net model. It achieves an accuracy of 98.78% for lung image segmentation, 95.96% for cell nuclei segmentation in microscope images (DSB 2018), and 98.58% for liver image segmentation by using merely 0.24%, 0.48%, and 0.67% of the number of parameters in the original U-Net architecture for the lung image segmentation dataset, the DSB 2018 dataset, and the liver image segmentation dataset, respectively. This reduction in complexity makes our proposed approach, GA-UNet, a more viable option for deployment in resource-limited environments or real-world implementations that demand more efficient and faster inference times.
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ISSN:2075-4426
2075-4426
DOI:10.3390/jpm13091298