SVD Based Least Squares for X-Ray Pneumonia Classification Using Deep Features
This work addresses the need for efficient and accurate automated diagnostic tools for pneumonia in medical imaging, though it is incremental as it builds on existing deep feature methods.
The paper tackled pneumonia classification from X-ray images by proposing a Singular Value Decomposition-based Least Squares (SVD-LS) framework, which achieved competitive performance with significantly reduced computational costs, making it suitable for real-time medical applications.
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.