干旱区生态环境知识资源中心(Arid): Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline

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	Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline
	Jain, Manju; Rai, C. S.; Jain, Jai; Gambhir, Deepak
通讯作者	Jain, M (corresponding author), Guru Gobind Singh Indraprastha Univ, Univ Sch Informat Commun & Technol, Delhi, India.
来源期刊	INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
ISSN	2158-107X
EISSN	2156-5570
出版年	2021
卷号	12 期号:1 页码:114-130
英文摘要	Brain atrophy is the degradation of brain cells and tissues to the extent that it is clearly indicative during Mini-Mental State Exam test and other psychological analysis. It is an alarming state of the human brain that progressively results in Alzheimer disease which is not curable. But timely detection of brain atrophy can help millions of people before they reach the state of Alzheimer. In this study we analyzed the longitudinal structural MRI of older adults in the age group of 42 to 96 of OASIS 3 Open Access Database. The nth slice of one subject does not match with the nth slice of another subject because the head position under the magnetic field is not synchronized. As a radiologist analyzes the MRI image data slice wise so our system also compares the MRI images slice wise, we deduced a method of slice by slice registration by driving mid slice location in each MRI image so that slices from different MRI images can be compared with least error. Machine learning is the technique which helps to exploit the information available in abundance of data and it can detect patterns in data which can give indication and detection of particular events and states. Each slice of MRI analyzed using simple statistical determinants and Gray level Co-Occurrence Matrix based statistical texture features from whole brain MRI images. The study explored varied classifiers Support Vector Machine, Random Forest, K-nearest neighbor, Naive Bayes, AdaBoost and Bagging Classifier methods to predict how normal brain atrophy differs from brain atrophy causing cognitive impairment. Different hyper parameters of classifiers tuned to get the best results. The study indicates Support Vector Machine and AdaBoost the most promising classifier to be used for automatic medical image analysis and early detection of brain diseases. The AdaBoost gives accuracy of 96.76% with specificity 95.87% and sensitivity 87.37% and receiving operating curve accuracy 96.3%. The SVM gives accuracy of 96% with 92% specificity and 87% sensitivity and receiving operating curve accuracy 95.05%.
英文关键词	Brain atrophy registration Freesurfer GLCM texture features FDR decision support system SVM AdaBoost Randomforest Bagging KNN Naive Bayes classification hyperparameters GridsearchCV Sklearn Python
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000621697400015
WOS关键词	DEFORMATION-BASED MORPHOMETRY ; PREDICT ALZHEIMERS-DISEASE ; BRAIN ATROPHY ; CLASSIFICATION
WOS类目	Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352848
作者单位	[Jain, Manju; Rai, C. S.] Guru Gobind Singh Indraprastha Univ, Univ Sch Informat Commun & Technol, Delhi, India; [Jain, Jai] Media Agil India Ltd, New Delhi, India; [Gambhir, Deepak] Galgotia Coll Engn & Technol, Utter Pradesh, India
推荐引用方式 GB/T 7714	Jain, Manju,Rai, C. S.,Jain, Jai,et al. Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline[J],2021,12(1):114-130.
APA	Jain, Manju,Rai, C. S.,Jain, Jai,&Gambhir, Deepak.(2021).Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline.INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS,12(1),114-130.
MLA	Jain, Manju,et al."Amalgamation of Machine Learning and Slice-by-Slice Registration of MRI for Early Prognosis of Cognitive Decline".INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 12.1(2021):114-130.