MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning

This study demonstrated that a classification model using a deep learning network can be effective in achieving differential diagnosis of SCH and MEN. Thus, the new method has great potential for developing new computer-aided diagnosis and applications with future clinical practice.

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Publication Details

Journal
Journal of X-Ray Science and Technology
Published
2024-11-27
DOI
https://doi.org/10.1177/08953996241289745
Citations
4
Primary Topic
Neurofibromatosis and Schwannoma Cases
Type
article
Field-Weighted Citation Impact
1.48

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article

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning

Zhenhua Zhou, Yidan Liu, Yuanjun Wang
4 citations
Journal of X-Ray Science and Technology
Neurofibromatosis and Schwannoma Cases
1.48
article

MRI classification and discrimination of spinal schwannoma and meningioma based on deep learning

Zhenhua Zhou, Yidan Liu, Yuanjun Wang
article en
4 citations

Abstract

This study demonstrated that a classification model using a deep learning network can be effective in achieving differential diagnosis of SCH and MEN. Thus, the new method has great potential for developing new computer-aided diagnosis and applications with future clinical practice.

Journal of X-Ray Science and TechnologyVol. 33(1)
University of Shanghai for Science and Technology (CN), Shanghai Changzheng Hospital (CN)
Natural Science Foundation of Shanghai
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 16%
Neurofibromatosis and Schwannoma Cases
1.48
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