From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images

The proposed hybrid CNN-ViT model demonstrates substantial potential in advancing the accuracy and robustness of CAD systems for chest disease diagnosis. By integrating CNN and ViT architectures, our approach enhances the diagnostic precision, which may help to alleviate the burden on healthcare systems in resource-limited settings and improve patient outcomes in chest disease diagnosis.

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Publication Details

Journal
Diagnostics
Published
2024-12-06
DOI
https://doi.org/10.3390/diagnostics14232754
Citations
26
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
10.51
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article

From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images

Neesrin Ali Kurdi, Akram Bennour, Mohammed Al-Sarem, Abdulaziz Aborujilah et al.
26 citations
Diagnostics
COVID-19 diagnosis using AI
10.51
article

From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images

Neesrin Ali Kurdi, Akram Bennour, Mohammed Al-Sarem, Abdulaziz Aborujilah, Tahar Mekhaznia, Mohamed Amroune, Yousra Hadhoud
article en
26 citations

Abstract

The proposed hybrid CNN-ViT model demonstrates substantial potential in advancing the accuracy and robustness of CAD systems for chest disease diagnosis. By integrating CNN and ViT architectures, our approach enhances the diagnostic precision, which may help to alleviate the burden on healthcare systems in resource-limited settings and improve patient outcomes in chest disease diagnosis.

DiagnosticsVol. 14(23)
Taibah University (SA), Université Larbi Tébessi (DZ), Yemenia University (YE), Dhofar University (OM)
Partnerships for the goals
Openalex Percentile: Top 1%
COVID-19 diagnosis using AI
10.51
DOIOpen Access PDFOpenAlex
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From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images — Neesrin Ali Kurdi, Akram Bennour, et al. · Diagnostics (2024) | TGRS Research Map | TGRS