干旱区生态环境知识资源中心(Arid): An Enhanced Machine Learning Approach for Brain MRI Classification

Arid

DOI	10.3390/diagnostics12112791
	An Enhanced Machine Learning Approach for Brain MRI Classification
	Siddiqi, Muhammad Hameed; Azad, Mohammad; Alhwaiti, Yousef
通讯作者	Siddiqi, MH
来源期刊	DIAGNOSTICS
EISSN	2075-4418
出版年	2022
卷号	12 期号:11
英文摘要	Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space's dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.
英文关键词	brain MRI medical imaging feature extraction recognition healthcare
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000894902100001
WOS关键词	GENERATIVE ADVERSARIAL NETWORKS ; TUMOR CLASSIFICATION ; FEATURE-EXTRACTION
WOS类目	Medicine, General & Internal
WOS研究方向	General & Internal Medicine
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392239
推荐引用方式 GB/T 7714	Siddiqi, Muhammad Hameed,Azad, Mohammad,Alhwaiti, Yousef. An Enhanced Machine Learning Approach for Brain MRI Classification[J],2022,12(11).
APA	Siddiqi, Muhammad Hameed,Azad, Mohammad,&Alhwaiti, Yousef.(2022).An Enhanced Machine Learning Approach for Brain MRI Classification.DIAGNOSTICS,12(11).
MLA	Siddiqi, Muhammad Hameed,et al."An Enhanced Machine Learning Approach for Brain MRI Classification".DIAGNOSTICS 12.11(2022).