MV-MFF: Multi-View Multi-Feature Fusion Model for Pneumonia Classification
Autor: | Najla Alsulami, Hassan Althobaiti, Tarik Alafif |
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Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Diagnostics, Vol 14, Iss 14, p 1566 (2024) |
Druh dokumentu: | article |
ISSN: | 14141566 2075-4418 |
DOI: | 10.3390/diagnostics14141566 |
Popis: | Pneumonia ranks among the most prevalent lung diseases and poses a significant concern since it is one of the diseases that may lead to death around the world. Diagnosing pneumonia necessitates a chest X-ray and substantial expertise to ensure accurate assessments. Despite the critical role of lateral X-rays in providing additional diagnostic information alongside frontal X-rays, they have not been widely used. Obtaining X-rays from multiple perspectives is crucial, significantly improving the precision of disease diagnosis. In this paper, we propose a multi-view multi-feature fusion model (MV-MFF) that integrates latent representations from a variational autoencoder and a β-variational autoencoder. Our model aims to classify pneumonia presence using multi-view X-rays. Experimental results demonstrate that the MV-MFF model achieves an accuracy of 80.4% and an area under the curve of 0.775, outperforming current state-of-the-art methods. These findings underscore the efficacy of our approach in improving pneumonia diagnosis through multi-view X-ray analysis. |
Databáze: | Directory of Open Access Journals |
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