SVD Based Least Squares for X-Ray Pneumonia Classification Using Deep Features
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918130087886848 |
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| author | Erdogan, Mete Demirtas, Sebnem |
| author_facet | Erdogan, Mete Demirtas, Sebnem |
| contents | Accurate and early diagnosis of pneumonia through X-ray imaging is essential for effective treatment and improved patient outcomes. Recent advancements in machine learning have enabled automated diagnostic tools that assist radiologists in making more reliable and efficient decisions. In this work, we propose a Singular Value Decomposition-based Least Squares (SVD-LS) framework for multi-class pneumonia classification, leveraging powerful feature representations from state-of-the-art self-supervised and transfer learning models. Rather than relying on computationally expensive gradient-based fine-tuning, we employ a closed-form, non-iterative classification approach that ensures efficiency without compromising accuracy. Experimental results demonstrate that SVD-LS achieves competitive performance while offering significantly reduced computational costs, making it a viable alternative for real-time medical imaging applications. The implementation is available at: github.com/meterdogan07/SVD-LS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20970 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SVD Based Least Squares for X-Ray Pneumonia Classification Using Deep Features Erdogan, Mete Demirtas, Sebnem Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Accurate and early diagnosis of pneumonia through X-ray imaging is essential for effective treatment and improved patient outcomes. Recent advancements in machine learning have enabled automated diagnostic tools that assist radiologists in making more reliable and efficient decisions. In this work, we propose a Singular Value Decomposition-based Least Squares (SVD-LS) framework for multi-class pneumonia classification, leveraging powerful feature representations from state-of-the-art self-supervised and transfer learning models. Rather than relying on computationally expensive gradient-based fine-tuning, we employ a closed-form, non-iterative classification approach that ensures efficiency without compromising accuracy. Experimental results demonstrate that SVD-LS achieves competitive performance while offering significantly reduced computational costs, making it a viable alternative for real-time medical imaging applications. The implementation is available at: github.com/meterdogan07/SVD-LS. |
| title | SVD Based Least Squares for X-Ray Pneumonia Classification Using Deep Features |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.20970 |