A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI.

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Title: A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI.
Authors: Tabarestani M; 1Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran., Delfan N; 1Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran.; 2School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran., Khoshnevisan M; 3Physics Department, College of Science, Northeastern University, Boston, Massachusetts.; 4Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey., Hatam Parikhan J; 5Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran., Bahri A; 6Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; and., Hessam A; 5Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran., Nabiuni M; 5Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran., Moshiri B; 2School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran.; 7Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada.
Source: Journal of neurosurgery. Spine [J Neurosurg Spine] 2026 Mar 27; Vol. 44 (6), pp. 847-857. Date of Electronic Publication: 2026 Mar 27.
Publication Type: Journal Article
Journal Info: Publisher: American Association of Neurological Surgeons Country of Publication: United States NLM ID: 101223545 Publication Model: Electronic Cited Medium: Internet ISSN: 1547-5646 (Electronic) Linking ISSN: 15475646 NLM ISO Abbreviation: J Neurosurg Spine Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI.
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  Data: <searchLink fieldCode="AU" term="%22Tabarestani+M%22">Tabarestani M</searchLink>; 1Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran.<br /><searchLink fieldCode="AU" term="%22Delfan+N%22">Delfan N</searchLink>; 1Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran.; 2School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran.<br /><searchLink fieldCode="AU" term="%22Khoshnevisan+M%22">Khoshnevisan M</searchLink>; 3Physics Department, College of Science, Northeastern University, Boston, Massachusetts.; 4Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey.<br /><searchLink fieldCode="AU" term="%22Hatam+Parikhan+J%22">Hatam Parikhan J</searchLink>; 5Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran.<br /><searchLink fieldCode="AU" term="%22Bahri+A%22">Bahri A</searchLink>; 6Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; and.<br /><searchLink fieldCode="AU" term="%22Hessam+A%22">Hessam A</searchLink>; 5Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran.<br /><searchLink fieldCode="AU" term="%22Nabiuni+M%22">Nabiuni M</searchLink>; 5Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran.<br /><searchLink fieldCode="AU" term="%22Moshiri+B%22">Moshiri B</searchLink>; 2School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran.; 7Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22American+Association+of+Neurological+Surgeons%22">American Association of Neurological Surgeons </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>101223545 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1547-5646 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2215475646%22">15475646 </searchLink><i>NLM ISO Abbreviation: </i>J Neurosurg Spine <i>Subsets: </i>MEDLINE
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        Value: 10.3171/2025.10.SPINE25878
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        Text: English
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      – TitleFull: A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI.
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            – D: 27
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              Text: 2026 Mar 27
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              Y: 2026
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