Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-020-10351-4.pdf
Reference81 articles.
1. Abd-Ellah MK et al (2016) Design and implementation of a computer-aided diagnosis system for brain tumor classification. In 2016 28th International Conference on Microelectronics (ICM). IEEE
2. Abdullah N, Ngah UK, Aziz SA (2011) Image classification of brain MRI using support vector machine. In 2011 IEEE International Conference on Imaging Systems and Techniques. IEEE
3. Aborisade D et al (2014) Comparative analysis of textural features derived from GLCM for ultrasound liver image classification. Energy 2:10
4. Aja-Fernández S, Alberola-López C, Westin C-F (2008) Noise and signal estimation in magnitude MRI and Rician distributed images: a LMMSE approach. IEEE Trans Image Process 17(8):1383–1398
5. Anisha K, Wilscy M (2011) Impulse noise removal from medical images using fuzzy genetic algorithm. Int J Multimed Appl 3(4):93–106
Cited by 47 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation;Multimedia Tools and Applications;2024-09-12
2. Brain Tumor Segmentation and Classification Using CNN Pre-Trained VGG-16 Model in MRI Images;IIUM Engineering Journal;2024-07-14
3. Optimizing U-Net CNN performance: a comparative study of noise filtering techniques for enhanced thermal image analysis;The Journal of Supercomputing;2024-07-05
4. An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique;Frontiers in Computational Neuroscience;2024-06-26
5. Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels;Plant Methods;2024-05-21
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3