Cargando…

Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke

Brain computed tomography (CT) is commonly used for evaluating the cerebral condition, but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled neuroradiologists. Deep learning networks are commonly employed for medical image analysis because they enable effi...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Yung-Ting, Chen, Yao-Liang, Chen, Yi-Yun, Huang, Yu-Ting, Wong, Ho-Fai, Yan, Jiun-Lin, Wang, Jiun-Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026481/
https://www.ncbi.nlm.nih.gov/pubmed/35453855
http://dx.doi.org/10.3390/diagnostics12040807
_version_ 1784691133337567232
author Chen, Yung-Ting
Chen, Yao-Liang
Chen, Yi-Yun
Huang, Yu-Ting
Wong, Ho-Fai
Yan, Jiun-Lin
Wang, Jiun-Jie
author_facet Chen, Yung-Ting
Chen, Yao-Liang
Chen, Yi-Yun
Huang, Yu-Ting
Wong, Ho-Fai
Yan, Jiun-Lin
Wang, Jiun-Jie
author_sort Chen, Yung-Ting
collection PubMed
description Brain computed tomography (CT) is commonly used for evaluating the cerebral condition, but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled neuroradiologists. Deep learning networks are commonly employed for medical image analysis because they enable efficient computer-aided diagnosis. This study proposed the use of convolutional neural network (CNN)-based deep learning models for efficient classification of strokes based on unenhanced brain CT image findings into normal, hemorrhage, infarction, and other categories. The included CNN models were CNN-2, VGG-16, and ResNet-50, all of which were pretrained through transfer learning with various data sizes, mini-batch sizes, and optimizers. Their performance in classifying unenhanced brain CT images was tested thereafter. This performance was then compared with the outcomes in other studies on deep learning–based hemorrhagic or ischemic stroke diagnoses. The results revealed that among our CNN-2, VGG-16, and ResNet-50 analyzed by considering several hyperparameters and environments, the CNN-2 and ResNet-50 outperformed the VGG-16, with an accuracy of 0.9872; however, ResNet-50 required a longer time to present the outcome than did the other networks. Moreover, our models performed much better than those reported previously. In conclusion, after appropriate hyperparameter optimization, our deep learning–based models can be applied to clinical scenarios where neurologist or radiologist may need to verify whether their patients have a hemorrhage stroke, an infarction, and any other symptom.
format Online
Article
Text
id pubmed-9026481
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90264812022-04-23 Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke Chen, Yung-Ting Chen, Yao-Liang Chen, Yi-Yun Huang, Yu-Ting Wong, Ho-Fai Yan, Jiun-Lin Wang, Jiun-Jie Diagnostics (Basel) Article Brain computed tomography (CT) is commonly used for evaluating the cerebral condition, but immediately and accurately interpreting emergent brain CT images is tedious, even for skilled neuroradiologists. Deep learning networks are commonly employed for medical image analysis because they enable efficient computer-aided diagnosis. This study proposed the use of convolutional neural network (CNN)-based deep learning models for efficient classification of strokes based on unenhanced brain CT image findings into normal, hemorrhage, infarction, and other categories. The included CNN models were CNN-2, VGG-16, and ResNet-50, all of which were pretrained through transfer learning with various data sizes, mini-batch sizes, and optimizers. Their performance in classifying unenhanced brain CT images was tested thereafter. This performance was then compared with the outcomes in other studies on deep learning–based hemorrhagic or ischemic stroke diagnoses. The results revealed that among our CNN-2, VGG-16, and ResNet-50 analyzed by considering several hyperparameters and environments, the CNN-2 and ResNet-50 outperformed the VGG-16, with an accuracy of 0.9872; however, ResNet-50 required a longer time to present the outcome than did the other networks. Moreover, our models performed much better than those reported previously. In conclusion, after appropriate hyperparameter optimization, our deep learning–based models can be applied to clinical scenarios where neurologist or radiologist may need to verify whether their patients have a hemorrhage stroke, an infarction, and any other symptom. MDPI 2022-03-25 /pmc/articles/PMC9026481/ /pubmed/35453855 http://dx.doi.org/10.3390/diagnostics12040807 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Yung-Ting
Chen, Yao-Liang
Chen, Yi-Yun
Huang, Yu-Ting
Wong, Ho-Fai
Yan, Jiun-Lin
Wang, Jiun-Jie
Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke
title Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke
title_full Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke
title_fullStr Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke
title_full_unstemmed Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke
title_short Deep Learning–Based Brain Computed Tomography Image Classification with Hyperparameter Optimization through Transfer Learning for Stroke
title_sort deep learning–based brain computed tomography image classification with hyperparameter optimization through transfer learning for stroke
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026481/
https://www.ncbi.nlm.nih.gov/pubmed/35453855
http://dx.doi.org/10.3390/diagnostics12040807
work_keys_str_mv AT chenyungting deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke
AT chenyaoliang deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke
AT chenyiyun deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke
AT huangyuting deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke
AT wonghofai deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke
AT yanjiunlin deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke
AT wangjiunjie deeplearningbasedbraincomputedtomographyimageclassificationwithhyperparameteroptimizationthroughtransferlearningforstroke