SIFT-AD: Scale-Invariant Feature Transform for Automatic Detection of Lung Diseases in X-ray Images

Chest X-rays are a crucial tool for diagnosing lung diseases. This study explores a novel approach that combines the strengths of two powerful techniques: Scale-Invariant Feature Transform (SIFT) and U-Net architecture. U-Net, a deep learning expert in image segmentation, effortlessly isolates lung regions within chest X-rays. SIFT, a computer vision technology, can potentially pinpoint areas suggestive of lung issues by identifying and describing key image features. The proposed method likely utilizes U-Net for precise lung segmentation, followed by SIFT analysis on the isolated lung region. This combined approach leverages U-Net’s ability to focus on lung tissue and SIFT’s strength in detecting characteristic signatures of lung disease. By evaluating the effectiveness of this U-Net and SIFT-based approach for lung disease detection, this study holds promise for improving diagnostic accuracy and assisting radiologists in their evaluations.