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Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine
Aim: Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment. Method: In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leverage...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461250/ https://www.ncbi.nlm.nih.gov/pubmed/34566615 http://dx.doi.org/10.3389/fncom.2021.738885 |
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author | Lu, Siyuan Liu, Shuaiqi Wang, Shui-Hua Zhang, Yu-Dong |
author_facet | Lu, Siyuan Liu, Shuaiqi Wang, Shui-Hua Zhang, Yu-Dong |
author_sort | Lu, Siyuan |
collection | PubMed |
description | Aim: Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment. Method: In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leveraged a sliding window to obtain training and testing samples from input brain images. Then, a 13-layer convolutional neural network (CNN) was designed and trained. Finally, we proposed to utilize an extreme learning machine (ELM) to substitute the last several layers in the CNN for detection. We carried out an experiment to decide the optimal number of layers to be substituted. The parameters in ELM were optimized by a heuristic algorithm named bat algorithm. The evaluation of our approach was based on hold-out validation, and the final predictions were generated by averaging the performance of five runs. Results: Through the experiments, we found replacing the last five layers with ELM can get the optimal results. Conclusion: We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection. |
format | Online Article Text |
id | pubmed-8461250 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84612502021-09-25 Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine Lu, Siyuan Liu, Shuaiqi Wang, Shui-Hua Zhang, Yu-Dong Front Comput Neurosci Neuroscience Aim: Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment. Method: In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leveraged a sliding window to obtain training and testing samples from input brain images. Then, a 13-layer convolutional neural network (CNN) was designed and trained. Finally, we proposed to utilize an extreme learning machine (ELM) to substitute the last several layers in the CNN for detection. We carried out an experiment to decide the optimal number of layers to be substituted. The parameters in ELM were optimized by a heuristic algorithm named bat algorithm. The evaluation of our approach was based on hold-out validation, and the final predictions were generated by averaging the performance of five runs. Results: Through the experiments, we found replacing the last five layers with ELM can get the optimal results. Conclusion: We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection. Frontiers Media S.A. 2021-09-10 /pmc/articles/PMC8461250/ /pubmed/34566615 http://dx.doi.org/10.3389/fncom.2021.738885 Text en Copyright © 2021 Lu, Liu, Wang and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Lu, Siyuan Liu, Shuaiqi Wang, Shui-Hua Zhang, Yu-Dong Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine |
title | Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine |
title_full | Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine |
title_fullStr | Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine |
title_full_unstemmed | Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine |
title_short | Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine |
title_sort | cerebral microbleed detection via convolutional neural network and extreme learning machine |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8461250/ https://www.ncbi.nlm.nih.gov/pubmed/34566615 http://dx.doi.org/10.3389/fncom.2021.738885 |
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