From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images Article Swipe
Yousra Hadhoud
,
Tahar Mekhaznia
,
Akram Bennour
,
Mohamed Amroune
,
Neesrin Ali Kurdi
,
Abdulaziz Aborujilah
,
Mohammed Al-Sarem
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14232754
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14232754
Background/Objectives: Chest disease identification for Tuberculosis and Pneumonia diseases presents diagnostic challenges due to overlapping radiographic features and the limited availability of expert radiologists, especially in developing countries. The present study aims to address these challenges by developing a Computer-Aided Diagnosis (CAD) system to provide consistent and objective analyses of chest X-ray images, thereby reducing potential human error. By leveraging the complementary strengths of convolutional neural networks (CNNs) and vision transformers (ViTs), we propose a hybrid model for the accurate detection of Tuberculosis and for distinguishing between Tuberculosis and Pneumonia. Methods: We designed a two-step hybrid model that integrates the ResNet-50 CNN with the ViT-b16 architecture. It uses the transfer learning on datasets from Guangzhou Women’s and Children’s Medical Center for Pneumonia cases and datasets from Qatar and Dhaka (Bangladesh) universities for Tuberculosis cases. CNNs capture hierarchical structures in images, while ViTs, with their self-attention mechanisms, excel at identifying relationships between features. Combining these approaches enhances the model’s performance on binary and multi-class classification tasks. Results: Our hybrid CNN-ViT model achieved a binary classification accuracy of 98.97% for Tuberculosis detection. For multi-class classification, distinguishing between Tuberculosis, viral Pneumonia, and bacterial Pneumonia, the model achieved an accuracy of 96.18%. These results underscore the model’s potential in improving diagnostic accuracy and reliability for chest disease classification based on X-ray images. Conclusions: 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.
Related Topics
Concepts
Convolutional neural network
Artificial intelligence
Computer science
Tuberculosis
Binary classification
Computer-aided diagnosis
CAD
Machine learning
Pattern recognition (psychology)
Contextual image classification
Deep learning
Transfer of learning
Binary number
Robustness (evolution)
Pathology
Medicine
Support vector machine
Image (mathematics)
Mathematics
Engineering
Chemistry
Engineering drawing
Biochemistry
Arithmetic
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14232754
- OA Status
- gold
- Cited By
- 14
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405108716
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405108716Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics14232754Digital Object Identifier
- Title
-
From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-06Full publication date if available
- Authors
-
Yousra Hadhoud, Tahar Mekhaznia, Akram Bennour, Mohamed Amroune, Neesrin Ali Kurdi, Abdulaziz Aborujilah, Mohammed Al-SaremList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics14232754Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/diagnostics14232754Direct OA link when available
- Concepts
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Convolutional neural network, Artificial intelligence, Computer science, Tuberculosis, Binary classification, Computer-aided diagnosis, CAD, Machine learning, Pattern recognition (psychology), Contextual image classification, Deep learning, Transfer of learning, Binary number, Robustness (evolution), Pathology, Medicine, Support vector machine, Image (mathematics), Mathematics, Engineering, Chemistry, Engineering drawing, Biochemistry, Arithmetic, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 14Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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