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TReC: Transferred ResNet and CBAM for Detecting Brain Diseases
Early diagnosis of pathological brains leads to early interventions in brain diseases, which may help control the illness conditions, prolong the life of patients, and even cure them. Therefore, the classification of brain diseases is a challenging but helpful task. However, it is hard to collect br...
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/PMC8733727/ https://www.ncbi.nlm.nih.gov/pubmed/35002667 http://dx.doi.org/10.3389/fninf.2021.781551 |
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author | Xiao, Yuteng Yin, Hongsheng Wang, Shui-Hua Zhang, Yu-Dong |
author_facet | Xiao, Yuteng Yin, Hongsheng Wang, Shui-Hua Zhang, Yu-Dong |
author_sort | Xiao, Yuteng |
collection | PubMed |
description | Early diagnosis of pathological brains leads to early interventions in brain diseases, which may help control the illness conditions, prolong the life of patients, and even cure them. Therefore, the classification of brain diseases is a challenging but helpful task. However, it is hard to collect brain images, and the superabundance of images is also a great challenge for computing resources. This study proposes a new approach named TReC: Transferred Residual Networks (ResNet)-Convolutional Block Attention Module (CBAM), a specific model for small-scale samples, to detect brain diseases based on MRI. At first, the ResNet model, which is pre-trained on the ImageNet dataset, serves as initialization. Subsequently, a simple attention mechanism named CBAM is introduced and added into every ResNet residual block. At the same time, the fully connected (FC) layers of the ResNet are replaced with new FC layers, which meet the goal of classification. Finally, all the parameters of our model, such as the ResNet, the CBAM, and new FC layers, are retrained. The effectiveness of the proposed model is evaluated on brain magnetic resonance (MR) datasets for multi-class and two-class tasks. Compared with other state-of-the-art models, our model reaches the best performance for two-class and multi-class tasks on brain diseases. |
format | Online Article Text |
id | pubmed-8733727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87337272022-01-07 TReC: Transferred ResNet and CBAM for Detecting Brain Diseases Xiao, Yuteng Yin, Hongsheng Wang, Shui-Hua Zhang, Yu-Dong Front Neuroinform Neuroscience Early diagnosis of pathological brains leads to early interventions in brain diseases, which may help control the illness conditions, prolong the life of patients, and even cure them. Therefore, the classification of brain diseases is a challenging but helpful task. However, it is hard to collect brain images, and the superabundance of images is also a great challenge for computing resources. This study proposes a new approach named TReC: Transferred Residual Networks (ResNet)-Convolutional Block Attention Module (CBAM), a specific model for small-scale samples, to detect brain diseases based on MRI. At first, the ResNet model, which is pre-trained on the ImageNet dataset, serves as initialization. Subsequently, a simple attention mechanism named CBAM is introduced and added into every ResNet residual block. At the same time, the fully connected (FC) layers of the ResNet are replaced with new FC layers, which meet the goal of classification. Finally, all the parameters of our model, such as the ResNet, the CBAM, and new FC layers, are retrained. The effectiveness of the proposed model is evaluated on brain magnetic resonance (MR) datasets for multi-class and two-class tasks. Compared with other state-of-the-art models, our model reaches the best performance for two-class and multi-class tasks on brain diseases. Frontiers Media S.A. 2021-12-23 /pmc/articles/PMC8733727/ /pubmed/35002667 http://dx.doi.org/10.3389/fninf.2021.781551 Text en Copyright © 2021 Xiao, Yin, 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 Xiao, Yuteng Yin, Hongsheng Wang, Shui-Hua Zhang, Yu-Dong TReC: Transferred ResNet and CBAM for Detecting Brain Diseases |
title | TReC: Transferred ResNet and CBAM for Detecting Brain Diseases |
title_full | TReC: Transferred ResNet and CBAM for Detecting Brain Diseases |
title_fullStr | TReC: Transferred ResNet and CBAM for Detecting Brain Diseases |
title_full_unstemmed | TReC: Transferred ResNet and CBAM for Detecting Brain Diseases |
title_short | TReC: Transferred ResNet and CBAM for Detecting Brain Diseases |
title_sort | trec: transferred resnet and cbam for detecting brain diseases |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733727/ https://www.ncbi.nlm.nih.gov/pubmed/35002667 http://dx.doi.org/10.3389/fninf.2021.781551 |
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