Pathological brain detection based on online sequential extreme learning machine

Siyuan Lu; Hainan Wang; Xueyan Wu; Shuihua Wang

doi:10.1109/PIC.2016.7949498

Pathological brain detection based on online sequential extreme learning machine

Siyuan Lu, Hainan Wang, Xueyan Wu, Shuihua Wang^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

2 Citations (Scopus)

Abstract

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

Original language	English
Title of host publication	PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing
Editors	Yinglin Wang, Yaoru Sun
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	219-223
Number of pages	5
ISBN (Electronic)	9781509034833
DOIs	https://doi.org/10.1109/PIC.2016.7949498
Publication status	Published - 15 Jun 2017
Externally published	Yes
Event	4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 - Shanghai, China Duration: 23 Dec 2016 → 25 Dec 2016

Publication series

Name	PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

Conference

Conference	4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016
Country/Territory	China
City	Shanghai
Period	23/12/16 → 25/12/16

Keywords

Classification
Magnetic resonance imagine
Online sequential extreme learning machine
Pattern recognition
Wavelet entropy

Access to Document

10.1109/PIC.2016.7949498

Cite this

Lu, S., Wang, H., Wu, X., & Wang, S. (2017). Pathological brain detection based on online sequential extreme learning machine. In Y. Wang, & Y. Sun (Eds.), PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing (pp. 219-223). Article 7949498 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PIC.2016.7949498

Lu, Siyuan ; Wang, Hainan ; Wu, Xueyan et al. / Pathological brain detection based on online sequential extreme learning machine. PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing. editor / Yinglin Wang ; Yaoru Sun. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 219-223 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing).

@inproceedings{ae332734307c425c8647b9ad05411755,

title = "Pathological brain detection based on online sequential extreme learning machine",

abstract = "Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.",

keywords = "Classification, Magnetic resonance imagine, Online sequential extreme learning machine, Pattern recognition, Wavelet entropy",

author = "Siyuan Lu and Hainan Wang and Xueyan Wu and Shuihua Wang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016 ; Conference date: 23-12-2016 Through 25-12-2016",

year = "2017",

month = jun,

day = "15",

doi = "10.1109/PIC.2016.7949498",

language = "English",

series = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "219--223",

editor = "Yinglin Wang and Yaoru Sun",

booktitle = "PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing",

}

Lu, S, Wang, H, Wu, X & Wang, S 2017, Pathological brain detection based on online sequential extreme learning machine. in Y Wang & Y Sun (eds), PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing., 7949498, PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing, Institute of Electrical and Electronics Engineers Inc., pp. 219-223, 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016, Shanghai, China, 23/12/16. https://doi.org/10.1109/PIC.2016.7949498

Pathological brain detection based on online sequential extreme learning machine. / Lu, Siyuan; Wang, Hainan; Wu, Xueyan et al.
PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing. ed. / Yinglin Wang; Yaoru Sun. Institute of Electrical and Electronics Engineers Inc., 2017. p. 219-223 7949498 (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Pathological brain detection based on online sequential extreme learning machine

AU - Lu, Siyuan

AU - Wang, Hainan

AU - Wu, Xueyan

AU - Wang, Shuihua

PY - 2017/6/15

Y1 - 2017/6/15

N2 - Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

AB - Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

KW - Classification

KW - Magnetic resonance imagine

KW - Online sequential extreme learning machine

KW - Pattern recognition

KW - Wavelet entropy

UR - http://www.scopus.com/inward/record.url?scp=85024502336&partnerID=8YFLogxK

U2 - 10.1109/PIC.2016.7949498

DO - 10.1109/PIC.2016.7949498

M3 - Conference Proceeding

AN - SCOPUS:85024502336

T3 - PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

SP - 219

EP - 223

BT - PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing

A2 - Wang, Yinglin

A2 - Sun, Yaoru

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Progress in Informatics and Computing, PIC 2016

Y2 - 23 December 2016 through 25 December 2016

ER -

Lu S, Wang H, Wu X, Wang S. Pathological brain detection based on online sequential extreme learning machine. In Wang Y, Sun Y, editors, PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing. Institute of Electrical and Electronics Engineers Inc. 2017. p. 219-223. 7949498. (PIC 2016 - Proceedings of the 2016 IEEE International Conference on Progress in Informatics and Computing). doi: 10.1109/PIC.2016.7949498

Pathological brain detection based on online sequential extreme learning machine

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this