Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation
<b>Background/Objectives:</b> Rapid and accurate assessment of lung diseases, like pneumonia, is critical for effective clinical decision-making, particularly during pandemics when disease progression can be severe. Early diagnosis plays a crucial role in preventing complications, necess...
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MDPI AG
2025-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/11/1301 |
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| author | Bouthaina Slika Fadi Dornaika Karim Hammoudi |
| author_facet | Bouthaina Slika Fadi Dornaika Karim Hammoudi |
| author_sort | Bouthaina Slika |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> Rapid and accurate assessment of lung diseases, like pneumonia, is critical for effective clinical decision-making, particularly during pandemics when disease progression can be severe. Early diagnosis plays a crucial role in preventing complications, necessitating the development of fast and efficient AI-based models for automated severity assessment. <b>Methods:</b> In this study, we introduce a novel approach that leverages VMamba, a state-of-the-art vision model based on the VisualStateSpace (VSS) framework and 2D-Selective-Scan (SS2D) spatial scanning, to enhance lung severity prediction. Integrated in a parallel multi-image regions approach, VMamba effectively captures global and local contextual features through structured state-space modeling, improving feature representation and robustness in medical image analysis. Additionally, we integrate a segmented lung replacement augmentation strategy to enhance data diversity and improve model generalization. The proposed method is trained on the RALO and COVID-19 datasets and compared against state-of-the-art models. <b>Results:</b> Experimental results demonstrate that our approach achieves superior performance, outperforming existing techniques in prediction accuracy and robustness. Key evaluation metrics, including Mean Absolute Error (MAE) and Pearson Correlation (PC), confirm the model’s effectiveness, while the incorporation of segmented lung replacement augmentation further enhances adaptability to diverse lung conditions. <b>Conclusions:</b> These findings highlight the potential of our method for reliable and immediate clinical applications in lung infection assessment. |
| format | Article |
| id | doaj-art-73c07ccc2fbf424589ee35d0b6f9c152 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-73c07ccc2fbf424589ee35d0b6f9c1522025-08-20T02:32:57ZengMDPI AGDiagnostics2075-44182025-05-011511130110.3390/diagnostics15111301Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement AugmentationBouthaina Slika0Fadi Dornaika1Karim Hammoudi2Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, 200018 San Sebastian, SpainDepartment of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, 200018 San Sebastian, SpainDepartment of Computer Science, Institut de Recherche en Informatique, Mathématiques, Automatique et Signal, Université de Haute-Alsace, 68093 Mulhouse, France<b>Background/Objectives:</b> Rapid and accurate assessment of lung diseases, like pneumonia, is critical for effective clinical decision-making, particularly during pandemics when disease progression can be severe. Early diagnosis plays a crucial role in preventing complications, necessitating the development of fast and efficient AI-based models for automated severity assessment. <b>Methods:</b> In this study, we introduce a novel approach that leverages VMamba, a state-of-the-art vision model based on the VisualStateSpace (VSS) framework and 2D-Selective-Scan (SS2D) spatial scanning, to enhance lung severity prediction. Integrated in a parallel multi-image regions approach, VMamba effectively captures global and local contextual features through structured state-space modeling, improving feature representation and robustness in medical image analysis. Additionally, we integrate a segmented lung replacement augmentation strategy to enhance data diversity and improve model generalization. The proposed method is trained on the RALO and COVID-19 datasets and compared against state-of-the-art models. <b>Results:</b> Experimental results demonstrate that our approach achieves superior performance, outperforming existing techniques in prediction accuracy and robustness. Key evaluation metrics, including Mean Absolute Error (MAE) and Pearson Correlation (PC), confirm the model’s effectiveness, while the incorporation of segmented lung replacement augmentation further enhances adaptability to diverse lung conditions. <b>Conclusions:</b> These findings highlight the potential of our method for reliable and immediate clinical applications in lung infection assessment.https://www.mdpi.com/2075-4418/15/11/1301pneumonialung diseasesautomatic predictionchest X-rayseverity quantificationmamba |
| spellingShingle | Bouthaina Slika Fadi Dornaika Karim Hammoudi Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation Diagnostics pneumonia lung diseases automatic prediction chest X-ray severity quantification mamba |
| title | Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation |
| title_full | Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation |
| title_fullStr | Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation |
| title_full_unstemmed | Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation |
| title_short | Parallel VMamba and Attention-Based Pneumonia Severity Prediction from CXRs: A Robust Model with Segmented Lung Replacement Augmentation |
| title_sort | parallel vmamba and attention based pneumonia severity prediction from cxrs a robust model with segmented lung replacement augmentation |
| topic | pneumonia lung diseases automatic prediction chest X-ray severity quantification mamba |
| url | https://www.mdpi.com/2075-4418/15/11/1301 |
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