scholarly journals Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks

2019 ◽  
pp. 112-120
Author(s):  
Qiang Huo ◽  
Geyu Tang ◽  
Feng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Swarm Optimization ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation

scholarly journals Retinal Vessel Segmentation Combined With Generative Adversarial Networks and Dense U-Net

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 194551-194560
Author(s):  
Xiaoyu Guo ◽  
Cheng Chen ◽  
Yuanyuan Lu ◽  
Ke Meng ◽  
Hongyu Chen ◽  
...  
Keyword(s):  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation

scholarly journals SERR-U-Net: Squeeze-and-Excitation Residual and Recurrent Block-Based U-Net for Automatic Vessel Segmentation in Retinal Image

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jinke Wang ◽  
Xiang Li ◽  
Peiqing Lv ◽  
Changfa Shi
Keyword(s):  
Retinal Image ◽  
Area Under The Curve ◽  
Super Resolution ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Retinal Vessel Segmentation ◽  
Block Based ◽  
Public Datasets

Background and Objective. Accurate segmentation of retinal vessels is considered as an important prerequisite for computer-aided diagnosis of ophthalmic diseases, diabetes, glaucoma, and other diseases. Although current learning-based methods have greatly improved the performance of retinal vessel segmentation, the accuracy could not meet the requirements of clinical assistance yet. Methods. A new SERR-U-Net framework for retinal vessel segmentation is proposed, which leverages technologies including Squeeze-and-Excitation (SE), residual module, and recurrent block. First, the convolution layers of encoder and decoder are modified on the basis of U-Net, and the recurrent block is used to increase the network depth. Second, the residual module is utilized to alleviate the vanishing gradient problem. Finally, to derive more specific vascular features, we employed the SE structure to introduce attention mechanism into the U-shaped network. In addition, enhanced super-resolution generative adversarial networks (ESRGANs) are also deployed to remove the noise of retinal image. Results. The effectiveness of this method was tested on two public datasets, DRIVE and STARE. In the experiment of DRIVE dataset, the accuracy and AUC (area under the curve) of our method were 0.9552 and 0.9784, respectively, and for SATRE dataset, 0.9796 and 0.9859 were achieved, respectively, which proved a high accuracy and promising prospect on clinical assistance. Conclusion. An improved U-Net network combining SE, ResNet, and recurrent technologies is developed for automatic vessel segmentation from retinal image. This new model enables an improvement on the accuracy compared to learning-based methods, and its robustness in circumvent challenging cases such as small blood vessels and intersection of vessels is also well demonstrated and validated.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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