RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Lu, Siyuan; Liu, Shuaiqi; Wang, Shui-Hua; Zhang, Yu-Dong

doi:10.3389/fncom.2021.738885

ORIGINAL RESEARCH article

Front. Comput. Neurosci. , 10 September 2021

Volume 15 - 2021 | https://doi.org/10.3389/fncom.2021.738885

RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Retracted

A retraction of this article was approved in:

Retraction: Cerebral microbleed detection via convolutional neural network and extreme learning machine
1. Read retraction

$\r\nSiyuan Lu&#x;$ Siyuan Lu^1†

Shuaiqi Liu^2†

Shui-Hua Wang^3*

Yu-Dong Zhang^1*

¹School of Informatics, University of Leicester, Leicester, United Kingdom
²College of Electronic and Information Engineering, Hebei University, Baoding, China
³School of Mathematics and Actuarial Science, University of Leicester, Leicester, United Kingdom

This article has been retracted. Please follow the link to the full retraction notice for details.

Citation: Lu S, Liu S, Wang S-H and Zhang Y-D (2021) Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine. Front. Comput. Neurosci. 15:738885. doi: 10.3389/fncom.2021.738885

Received: 09 July 2021; Accepted: 20 August 2021;
Published: 10 September 2021; Retracted: 28 December 2023.

Edited by:

Yewang Chen, Huaqiao University, China

Reviewed by:

S. S. Gao, Henan University, China
Guo Xing, Nanjing Normal University, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

RETRACTED: Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

94% of researchers rate our articles as excellent or good

94% of researchers rate our articles as excellent or good