Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding

Author:

Kamireddy Rasool ReddyORCID,Kandala Rajesh N. V. P. S.ORCID,Dhuli Ravindra,Polinati Srinivasu,Sonti Kamesh,Tadeusiewicz Ryszard,Pławiak Paweł

Abstract

Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.

Publisher

Public Library of Science (PLoS)

Reference48 articles.

1. The 2007 WHO classification of tumours of the central nervous system;DN Louis;Acta neuropathologica,2007

2. A comparative study of medical imaging techniques;H Kasban;International Journal of Information Science and Intelligent System,2015

3. A survey of brain tumor segmentation and classification algorithms;ES Biratu;Journal of Imaging,2021

4. An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images;G Vishnuvarthanan;Applied Soft Computing,2016

5. K-means clustering and neural network for object detecting and identifying abnormality of brain tumor;N Arunkumar;Soft Computing,2019

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