KEYWORDS: Image segmentation, Convolution, Blood vessels, Systems modeling, Data modeling, Image processing algorithms and systems, Education and training, Medical imaging, Image processing, Gamma correction
This paper propose a retinal image segmentation model according to hybrid pooling and multi-scale attention mechanism. Due to the crucial importance of retinal vessels for the segmentation of the retina and their intricate structure, which serves as vital diagnostic information for diseases, we focus on retinal images in medical imaging. We introduce a novel medical image segmentation model, MDA-Unet, built upon the Unet architecture. Extensive experiments are conducted on the DRIVE dataset. Addressing challenges such as limited data, varying image quality, and the presence of small structures in segmented target areas causing reduced segmentation accuracy, the proposed enhanced model MDA-Unet aims to overcome these issues. We investigate the impact of incorporating modules such as Exponential Moving Average (EMA), Multi-Path Module (MPM), and serpentine convolutional kernels on the model. The research results indicate that the addition of these modules enhances the ability of the model to be segmented. This improved model is tested with the DRIVE dataset, yielding segmentation results that surpass those of previously proposed segmentation models. Compared with the previous model segmentation results, the method proposed in this paper has achieved good results in fundus vascular segmentation.
The flexible job-shop scheduling problem surpasses the limitations of conventional workshop scheduling problems that reduce machine constraints, increase uncertainty, and belong to the NP-hard problem. An adaptive simulated annealing genetic algorithm based on reinforcement learning is put forward to overcome the constraints of complex parameter determination and poor local search capabilities that standard genetic algorithms face when dealing with flexible job shop scheduling. The approach uses a multi-parent POX crossover operation and introduces a simulated annealing algorithm into the mutation operation to enhance the method's capability for both global and local optimization in the evolution process. The tournament method is combined with the optimal strategy to ensure the algorithm's convergence. The crossover and mutation parameters are dynamically adjusted and optimized using the reinforcement learning algorithm in conjunction with the improved simulated annealing genetic algorithm so that the parameters of the algorithm can adapt to the evolution process according to experience, and the searchability and computational efficiency of the algorithm are improved. By testing and examining the common examples, the proposed algorithm's rationality and superiority are ultimately demonstrated.
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