干旱区生态环境知识资源中心(Arid): A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers

Arid

DOI	10.1155/2021/7965677
	A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers
	Jain, Manju; Rai, C. S.; Jain, Jai
通讯作者	Jain, M (corresponding author), Guru Gobind Singh Indraprastha Univ, Univ Coll Informat Commun & Technol, Dwarka Sect 16-C, New Delhi 110078, India. ; Jain, M (corresponding author), Meerabai Inst Technol Maharani Bagh, New Delhi 110065, India.
来源期刊	COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
ISSN	1748-670X
EISSN	1748-6718
出版年	2021
卷号	2021
英文摘要	We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000685192700001
WOS关键词	VOXEL-BASED MORPHOMETRY ; MRI ; VOLUMETRY ; THICKNESS ; DIAGNOSIS ; CORTEX ; AD
WOS类目	Mathematical & Computational Biology
WOS研究方向	Mathematical & Computational Biology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362894
作者单位	[Jain, Manju; Rai, C. S.] Guru Gobind Singh Indraprastha Univ, Univ Coll Informat Commun & Technol, Dwarka Sect 16-C, New Delhi 110078, India; [Jain, Manju] Meerabai Inst Technol Maharani Bagh, New Delhi 110065, India; [Jain, Jai] Media Agil India Ltd, New Delhi, India
推荐引用方式 GB/T 7714	Jain, Manju,Rai, C. S.,Jain, Jai. A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers[J],2021,2021.
APA	Jain, Manju,Rai, C. S.,&Jain, Jai.(2021).A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2021.
MLA	Jain, Manju,et al."A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers".COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021(2021).