Cargando…
Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification
Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700359/ https://www.ncbi.nlm.nih.gov/pubmed/34943615 http://dx.doi.org/10.3390/diagnostics11122379 |
_version_ | 1784620738097971200 |
---|---|
author | Dai, Yin Song, Yumeng Liu, Weibin Bai, Wenhe Gao, Yifan Dong, Xinyang Lv, Wenbo |
author_facet | Dai, Yin Song, Yumeng Liu, Weibin Bai, Wenhe Gao, Yifan Dong, Xinyang Lv, Wenbo |
author_sort | Dai, Yin |
collection | PubMed |
description | Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification. |
format | Online Article Text |
id | pubmed-8700359 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87003592021-12-24 Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification Dai, Yin Song, Yumeng Liu, Weibin Bai, Wenhe Gao, Yifan Dong, Xinyang Lv, Wenbo Diagnostics (Basel) Article Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification. MDPI 2021-12-17 /pmc/articles/PMC8700359/ /pubmed/34943615 http://dx.doi.org/10.3390/diagnostics11122379 Text en © 2021 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 Dai, Yin Song, Yumeng Liu, Weibin Bai, Wenhe Gao, Yifan Dong, Xinyang Lv, Wenbo Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification |
title | Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification |
title_full | Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification |
title_fullStr | Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification |
title_full_unstemmed | Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification |
title_short | Multi-Focus Image Fusion Based on Convolution Neural Network for Parkinson’s Disease Image Classification |
title_sort | multi-focus image fusion based on convolution neural network for parkinson’s disease image classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700359/ https://www.ncbi.nlm.nih.gov/pubmed/34943615 http://dx.doi.org/10.3390/diagnostics11122379 |
work_keys_str_mv | AT daiyin multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification AT songyumeng multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification AT liuweibin multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification AT baiwenhe multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification AT gaoyifan multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification AT dongxinyang multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification AT lvwenbo multifocusimagefusionbasedonconvolutionneuralnetworkforparkinsonsdiseaseimageclassification |