Deep learning-based brain tumor detection: An MRI segmentation approach

Author:

Swathi V.N.V.L.S.,Sinduja K.,Ravi Kumar V.,Mahendar A.,Prasad Gollanapalli V.,Samya Banoth

Abstract

The detection and segmentation of brain tumors from magnetic resonance imaging (MRI) scans are crucial for diagnosing, planning treatments, and monitoring patients with neurological disorders. This abstract provides a comprehensive overview of deep learning-based methods for detecting brain tumors, focusing on techniques for segmenting MRI images. Deep learning models, particularly convolutional neural networks (CNNs), have achieved impressive results in accurately segmenting brain tumors by learning distinctive features directly from the image data. Various CNN architectures, such as U-Net, DeepMedic, and 3D convolutional networks, have been specifically designed to address the challenges of brain tumor segmentation, including tumor heterogeneity, irregular shapes, and varying sizes. Additionally, the integration of multimodal MRI data, such as T1-weighted, T2-weighted, and FLAIR images, has enhanced the robustness and accuracy of deep learning models for brain tumor detection. This abstract discusses the significant advancements, challenges, and future directions in deep learning-based brain tumor detection, emphasizing the potential of MRI segmentation techniques to support clinicians in early diagnosis and personalized treatment planning for patients with brain tumors.

Publisher

EDP Sciences

Reference21 articles.

1. “Meningioma”, American Brain Tumor Association. http://www.abtatrialconnect.org Buades A., Coll B., Morel J.M., A review of image denoising algorithms, with a new one, Multiscale Model. Simul., 2005.

2. Rodriguez A.O., “Principles of magnetic resonance imaging”, Rev. Mex. Fis.2004. A.R.Kavitha, et al., “AnEfficient Approach for Brain Tumour Detection Based on Modified Region Growing and Network in MRI Images” IEEE, 2012.

3. Ella Hassanien Aboul, Abraham Ajith, Peters James F., Schaefer Gerald, Henry Christopher, “Rough Sets and Near Sets in Medical Imaging: A Review”, IEEE Trans. on Information Technology in Biomedicine, 2009.

4. Melouah Ahlem and Amirouche Radia, “Comparative study of automatic seed selection methods for medical image segmentation by region growing technique”, Recent Advances in Biology, Biomedicine and Bioengineering, 2014, pp 91-97.

同舟云学术

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