Brain pathology identification using computer aided diagnostic tool: A systematic review

被引：27

作者：

Gudigar, Anjan ^{[1
]}

Raghavendra, U. ^{[1
]}

Hegde, Ajay ^{[2
]}

Kalyani, M. ^{[1
]}

Ciaccio, Edward J. ^{[3
]}

Acharya, U. Rajendra ^{[4
,5
,6
]}

机构：

[1] Manipal Acad Higher Educ, Manipal Inst Technol, Dept Instrumentat & Control Engn, Manipal 576104, Karnataka, India

[2] NHS Greater Glasgow & Clyde, Inst Neurol Sci, Neurosurg, Glasgow, Lanark, Scotland

[3] Columbia Univ, Med Ctr, cDept Med, New York, NY USA

[4] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore 599489, Singapore

[5] SUSS Univ, Sch Sci & Technol, Dept Biomed Engn, Singapore 599491, Singapore

[6] Kumamoto Univ, IROAST, Kumamoto, Japan

来源：

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | 2020年 / 187卷

关键词：

Brain pathology; Computer aided diagnostic; Classification; Deep learning; Feature extraction; Magnetic resonance imaging; EXTREME LEARNING-MACHINE; EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINE; IMAGE CLASSIFICATION; NEURAL-NETWORK; SLANTLET TRANSFORM; WAVELET TRANSFORM; MR-IMAGES; FEATURES; FRAMEWORK;

D O I：

10.1016/j.cmpb.2019.105205

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Computer aided diagnostic (CAD) has become a significant tool in expanding patient quality-of-life by reducing human errors in diagnosis. CAD can expedite decision-making on complex clinical data automatically. Since brain diseases can be fatal, rapid identification of brain pathology to prolong patient life is an important research topic. Many algorithms have been proposed for efficient brain pathology identification (BPI) over the past decade. Constant refinement of the various image processing algorithms must take place to expand performance of the automatic BPI task. In this paper, a systematic survey of contemporary BPI algorithms using brain magnetic resonance imaging (MRI) is presented. A summarization of recent literature provides investigators with a helpful synopsis of the domain. Furthermore, to enhance the performance of BPI, future research directions are indicated. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：18

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[41] Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series [J].