基于遗传算法的鲁棒性最优特征预测模型，用于磁共振成像数据的脑肿瘤分类

IF 1.1 3区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Network-Computation in Neural Systems Pub Date : 2024-04-22 DOI:10.1080/0954898x.2024.2343340

Meenal Thayumanavan, Asokan Ramasamy

引用次数: 0

摘要

如果能对脑肿瘤进行初步筛查并及时治疗，脑肿瘤是可以治愈的。这一想法提出了一种基于变换和窗口的优化策略，用于发现和治疗脑肿瘤。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Network-Computation in Neural Systems 工程技术-工程：电子与电气

CiteScore

3.70

自引率

1.30%

发文量

审稿时长

>12 weeks

期刊介绍： Network: Computation in Neural Systems welcomes submissions of research papers that integrate theoretical neuroscience with experimental data, emphasizing the utilization of cutting-edge technologies. We invite authors and researchers to contribute their work in the following areas: Theoretical Neuroscience: This section encompasses neural network modeling approaches that elucidate brain function. Neural Networks in Data Analysis and Pattern Recognition: We encourage submissions exploring the use of neural networks for data analysis and pattern recognition, including but not limited to image analysis and speech processing applications. Neural Networks in Control Systems: This category encompasses the utilization of neural networks in control systems, including robotics, state estimation, fault detection, and diagnosis. Analysis of Neurophysiological Data: We invite submissions focusing on the analysis of neurophysiology data obtained from experimental studies involving animals. Analysis of Experimental Data on the Human Brain: This section includes papers analyzing experimental data from studies on the human brain, utilizing imaging techniques such as MRI, fMRI, EEG, and PET. Neurobiological Foundations of Consciousness: We encourage submissions exploring the neural bases of consciousness in the brain and its simulation in machines.