Genetic U-Net: Automatically Designing Lightweight U-shaped CNN Architectures Using the Genetic Algorithm for Retinal Vessel Segmentation
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Many previous works based on deep learning for retinal vessel segmentation have achieved promising performance by manually designing U-shaped convolutional neural networks (CNNs). However, the manual design of these CNNs is time-consuming and requires extensive empirical knowledge. To address this problem, we propose a novel method using genetic algorithms (GAs) to automatically design a lightweight U-shaped CNN for retinal vessel segmentation, called Genetic U-Net. Here we first design a special search space containing the structure of U-Net and its corresponding operations, and then use genetic algorithm to search for superior architectures in this search space. Experimental results show that the proposed method outperforms the existing methods on three public datasets, DRIVE, CHASE_DB1 and STARE. In addition, the architectures obtained by the proposed method are more lightweight but accurate than the state-of-the-art models.
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