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.

本研究解决了通过X射线影像准确及时诊断肺炎的难题，提出了一种基于奇异值分解的最小二乘法（SVD-LS）框架，利用最先进的自监督和迁移学习模型所提取的深度特征进行多类别肺炎分类。实验结果表明，SVD-LS在保持高准确率的同时显著降低了计算成本，成为实时医学影像应用的可行替代方案。

基于SVD的最小二乘法在X射线肺炎分类中的应用