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2024 | OriginalPaper | Buchkapitel

MvRNA: A New Multi-view Deep Neural Network for Predicting Parkinson’s Disease

verfasst von : Lin Chen, Yuxin Zhou, Xiaobo Zhang, Zhehao Zhang, Hailong Zheng

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

Magnetic Resonance Imaging (MRI) is a critical medical diagnostic tool that assists experts in precisely identifying lesions. However, due to its high-dimensional nature, it requires substantial storage resources. If only one MRI slice were to be used, a significant amount of information might be lost. To address these issues, we propose segmenting 3D MRI data and training these slices separately. We propose a new Multi-view Learning neural network based on ResNet and an Attention mechanism, called MvRNA. ResNet18 is selected as the backbone network, and the Squeeze-and-Excitation network is applied between blocks to extract features from slices. Additionally, we propose a new BWH (Basic Block with Hybrid Dilated Convolution) module to capture a broader range of receptive fields, thus acquiring additional spatial features. We obtained data from Parkinson’s Progression Markers Initiative (PPMI) and applied our method to distinguish between Healthy Control, Prodromal, and Parkinson’s disease patients. The experimental results demonstrate that our method achieved an accuracy of 81.84%.

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Titel: MvRNA: A New Multi-view Deep Neural Network for Predicting Parkinson’s Disease
verfasst von: Lin Chen
Yuxin Zhou
Xiaobo Zhang
Zhehao Zhang
Hailong Zheng
Verlag: Springer Nature Singapore
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-981-9722-52-5

Electronic ISBN: 978-981-9722-53-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2253-2_8

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