Network-Computation in Neural Systems最新文献_第9页

Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing. 基于分数社会优化的迁移和副本管理算法，用于云计算分布式文件系统的负载平衡。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-21 DOI: 10.1080/0954898X.2024.2353665

Manjula Hulagappa Nebagiri, Latha Pillappa Hnumanthappa

{"title":"Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing.","authors":"Manjula Hulagappa Nebagiri, Latha Pillappa Hnumanthappa","doi":"10.1080/0954898X.2024.2353665","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2353665","url":null,"abstract":"Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-28"},"PeriodicalIF":7.8,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach. 增强胸部X光图像中的多类肺病分类：混合蝠鲼觅食火山爆发算法增强多层感知器神经网络方法。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-16 DOI: 10.1080/0954898X.2024.2350579

Rajendran Thavasimuthu, Sudheer Hanumanthakari, Sridhar Sekar, Sakthivel Kirubakaran

{"title":"Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.","authors":"Rajendran Thavasimuthu, Sudheer Hanumanthakari, Sridhar Sekar, Sakthivel Kirubakaran","doi":"10.1080/0954898X.2024.2350579","DOIUrl":"10.1080/0954898X.2024.2350579","url":null,"abstract":"One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-rays. However, issues of stability and class imbalance still need to be resolved. Hence in this manuscript, multi-class lung disease classification in chest x-ray images using a hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach is proposed (MPNN-Hyb-MRF-VEA). Initially, the input chest X-ray images are taken from the Covid-Chest X-ray dataset. Anisotropic diffusion Kuwahara filtering (ADKF) is used to enhance the quality of these images and lower noise. To capture significant discriminative features, the Term frequency-inverse document frequency (TF-IDF) based feature extraction method is utilized in this case. The Multilayer Perceptron Neural Network (MPNN) serves as the classification model for multi-class lung disorders classification as COVID-19, pneumonia, tuberculosis (TB), and normal. A Hybrid Manta-Ray Foraging and Volcano Eruption Algorithm (Hyb-MRF-VEA) is introduced to further optimize and fine-tune the MPNN's parameters. The Python platform is used to accurately evaluate the proposed methodology. The performance of the proposed method provides 23.21%, 12.09%, and 5.66% higher accuracy compared with existing methods like NFM, SVM, and CNN respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-32"},"PeriodicalIF":1.1,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140946455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network. 使用基于对称卷积的启发式辅助残差注意网络的智能无线信道损坏图像去噪框架。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-14 DOI: 10.1080/0954898X.2024.2350578

Sreedhar Mala, Aparna Kukunuri

{"title":"An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network.","authors":"Sreedhar Mala, Aparna Kukunuri","doi":"10.1080/0954898X.2024.2350578","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2350578","url":null,"abstract":"Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the \"Symmetric Convolution-based Residual Attention Network (SC-RAN)\" is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-34"},"PeriodicalIF":7.8,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140923502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer. 使用单候选优化器优化的构成型人工神经网络进行 Omics 数据分类。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-12 DOI: 10.1080/0954898X.2024.2348726

Subramaniam Madhan, Anbarasan Kalaiselvan

{"title":"Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.","authors":"Subramaniam Madhan, Anbarasan Kalaiselvan","doi":"10.1080/0954898X.2024.2348726","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2348726","url":null,"abstract":"Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO) is proposed in this manuscript. The input data is pre-processing by using Adaptive variational Bayesian filtering (AVBF) to replace missing values. The pre-processing data is fed to Zebra Optimization Algorithm (ZOA) for dimensionality reduction. Then, the Constitutive Artificial Neural Network (CANN) is employed to classify omics data. The weight parameter is optimized by Single Candidate Optimizer (SCO). The proposed ODC-ZOA-CANN-SCO method attains 25.36%, 21.04%, 22.18%, 26.90%, and 28.12% higher accuracy when analysed to the existing methods like multi-omics data integration utilizing adaptive graph learning and attention mode for patient categorization with biomarker identification (MOD-AGL-AM-PABI), deep learning method depending upon multi-omics data integration to create risk stratification prediction mode for skin cutaneous melanoma (DL-MODI-RSP-SCM), Deep belief network-base model for identifying Alzheimer's disease utilizing multi-omics data (DDN-DAD-MOD), hybrid cancer prediction depending upon multi-omics data and reinforcement learning state action reward state action (HCP-MOD-RL-SARSA), machine learning basis method under omics data including biological knowledge database for cancer clinical endpoint prediction (ML-ODBKD-CCEP) methods, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-25"},"PeriodicalIF":7.8,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm. MCN 投资组合：使用混合元启发式优化算法的多串级联网络的高效投资组合预测和选择模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-08 DOI: 10.1080/0954898X.2024.2346115

Meeta Sharma, Pankaj Kumar Sharma, Hemant Kumar Vijayvergia, Amit Garg, Shyam Sundar Agarwal, Varun Prakash Saxena

{"title":"MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid meta-heuristic optimization algorithm.","authors":"Meeta Sharma, Pankaj Kumar Sharma, Hemant Kumar Vijayvergia, Amit Garg, Shyam Sundar Agarwal, Varun Prakash Saxena","doi":"10.1080/0954898X.2024.2346115","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2346115","url":null,"abstract":"Generally, financial investments are necessary for portfolio management. However, the prediction of a portfolio becomes complicated in several processing techniques which may cause certain issues while predicting the portfolio. Moreover, the error analysis needs to be validated with efficient performance measures. To solve the problems of portfolio optimization, a new portfolio prediction framework is developed. Initially, a dataset is collected from the standard database which is accumulated with various companies' portfolios. For forecasting the benefits of companies, a Multi-serial Cascaded Network (MCNet) is employed which constitutes of Autoencoder, 1D Convolutional Neural Network (1DCNN), and Recurrent Neural Network (RNN) is utilized. The prediction output for the different companies is stored using the developed MCNet model for further use. After predicting the benefits, the best company with the highest profit is selected by Integration of Artificial Rabbit and Hummingbird Algorithm (IARHA). The major contribution of our work is to increase the accuracy of prediction and to choose the optimal portfolio. The implementation is conducted in Python platform. The result analysis shows that the developed model achieves 0.89% and 0.56% regarding RMSE and MAE measures. Throughout the analysis, the experimentation of the developed model shows enriched performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-38"},"PeriodicalIF":7.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuromorphic computing spiking neural network edge detection model for content based image retrieval. 基于内容的图像检索的神经形态计算尖峰神经网络边缘检测模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-06 DOI: 10.1080/0954898X.2024.2348018

Ambuj, Rajendra Machavaram

{"title":"Neuromorphic computing spiking neural network edge detection model for content based image retrieval.","authors":"Ambuj, Rajendra Machavaram","doi":"10.1080/0954898X.2024.2348018","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2348018","url":null,"abstract":"In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-31"},"PeriodicalIF":7.8,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning. 下丘多单元活动对声音类别识别的神经解码与时间相关和迁移学习。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-11-20 DOI: 10.1080/0954898X.2023.2282576

Fatma Özcan, Ahmet Alkan

{"title":"Neural decoding of inferior colliculus multiunit activity for sound category identification with temporal correlation and transfer learning.","authors":"Fatma Özcan, Ahmet Alkan","doi":"10.1080/0954898X.2023.2282576","DOIUrl":"10.1080/0954898X.2023.2282576","url":null,"abstract":"Natural sounds are easily perceived and identified by humans and animals. Despite this, the neural transformations that enable sound perception remain largely unknown. It is thought that the temporal characteristics of sounds may be reflected in auditory assembly responses at the inferior colliculus (IC) and which may play an important role in identification of natural sounds. In our study, natural sounds will be predicted from multi-unit activity (MUA) signals collected in the IC. Data is obtained from an international platform publicly accessible. The temporal correlation values of the MUA signals are converted into images. We used two different segment sizes and with a denoising method, we generated four subsets for the classification. Using pre-trained convolutional neural networks (CNNs), features of the images were extracted and the type of heard sound was classified. For this, we applied transfer learning from Alexnet, Googlenet and Squeezenet CNNs. The classifiers support vector machines (SVM), k-nearest neighbour (KNN), Naive Bayes and Ensemble were used. The accuracy, sensitivity, specificity, precision and F1 score were measured as evaluation parameters. By using all the tests and removing the noise, the accuracy improved significantly. These results will allow neuroscientists to make interesting conclusions.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"101-133"},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques. 基于金鹰的改进型 Att-BiLSTM 模型，采用混合特征提取和特征选择技术进行大数据分类。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-28 DOI: 10.1080/0954898X.2023.2293895

Gnanendra Kotikam, Lokesh Selvaraj

{"title":"Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques.","authors":"Gnanendra Kotikam, Lokesh Selvaraj","doi":"10.1080/0954898X.2023.2293895","DOIUrl":"10.1080/0954898X.2023.2293895","url":null,"abstract":"The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"154-189"},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139059143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization-enabled deep learning model for disease detection in IoT platform. 用于物联网平台疾病检测的优化深度学习模型。

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-28 DOI: 10.1080/0954898X.2023.2296568

Amol Dattatray Dhaygude

{"title":"Optimization-enabled deep learning model for disease detection in IoT platform.","authors":"Amol Dattatray Dhaygude","doi":"10.1080/0954898X.2023.2296568","DOIUrl":"10.1080/0954898X.2023.2296568","url":null,"abstract":"Nowadays, Internet of things (IoT) and IoT platforms are extensively utilized in several healthcare applications. The IoT devices produce a huge amount of data in healthcare field that can be inspected on an IoT platform. In this paper, a novel algorithm, named artificial flora optimization-based chameleon swarm algorithm (AFO-based CSA), is developed for optimal path finding. Here, data are collected by the sensors and transmitted to the base station (BS) using the proposed AFO-based CSA, which is derived by integrating artificial flora optimization (AFO) in chameleon swarm algorithm (CSA). This integration refers to the AFO-based CSA model enhancing the strengths and features of both AFO and CSA for optimal routing of medical data in IoT. Moreover, the proposed AFO-based CSA algorithm considers factors such as energy, delay, and distance for the effectual routing of data. At BS, prediction is conducted, followed by stages, like pre-processing, feature dimension reduction, adopting Pearson's correlation, and disease detection, done by recurrent neural network, which is trained by the proposed AFO-based CSA. Experimental result exhibited that the performance of the proposed AFO-based CSA is superior to competitive approaches based on the energy consumption (0.538 J), accuracy (0.950), sensitivity (0.965), and specificity (0.937).","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"190-211"},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139059144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation. CS-UNet：用于视网膜血管分割的具有语义位置依赖性的跨尺度 U-Net

IF 7.8 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2024-05-01 Epub Date: 2023-12-05 DOI: 10.1080/0954898X.2023.2288858

Ying Yang, Shengbin Yue, Haiyan Quan

{"title":"CS-UNet: Cross-scale U-Net with Semantic-position dependencies for retinal vessel segmentation.","authors":"Ying Yang, Shengbin Yue, Haiyan Quan","doi":"10.1080/0954898X.2023.2288858","DOIUrl":"10.1080/0954898X.2023.2288858","url":null,"abstract":"Accurate retinal vessel segmentation is the prerequisite for early recognition and treatment of retina-related diseases. However, segmenting retinal vessels is still challenging due to the intricate vessel tree in fundus images, which has a significant number of tiny vessels, low contrast, and lesion interference. For this task, the u-shaped architecture (U-Net) has become the de-facto standard and has achieved considerable success. However, U-Net is a pure convolutional network, which usually shows limitations in global modelling. In this paper, we propose a novel Cross-scale U-Net with Semantic-position Dependencies (CS-UNet) for retinal vessel segmentation. In particular, we first designed a Semantic-position Dependencies Aggregator (SPDA) and incorporate it into each layer of the encoder to better focus on global contextual information by integrating the relationship of semantic and position. To endow the model with the capability of cross-scale interaction, the Cross-scale Relation Refine Module (CSRR) is designed to dynamically select the information associated with the vessels, which helps guide the up-sampling operation. Finally, we have evaluated CS-UNet on three public datasets: DRIVE, CHASE_DB1, and STARE. Compared to most existing state-of-the-art methods, CS-UNet demonstrated better performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"134-153"},"PeriodicalIF":7.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138489124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0