Review of Deep Learning and Multi-Scale Image Processing Techniques for Pulmonary Nodule Detection in Chest X-Ray and CT Imaging

Abstract: To increase the lung cancer survival rate, it is important to detect pulmonary nodules early; however, this remains a challenging task due to the constant fluctuation in nodule size, shape, and texture, and to the low contrast in chest X-ray (CXR) and computed tomography (CT) images. Current improvements in the deep learning technology have greatly influenced computer-aided detection systems by facilitating an automated hierarchical feature extraction, while being able to maintain multi-scale image processing techniques, which have increased the rapid identification of both small and circumstantially subtle nodes. This review paper investigates contemporary methods integrating convolutional neural networks, 3D frameworks, attention mechanisms, feature pyramid networks, and transformer-based models with multi-scale analysis approaches for powerful pulmonary nodule detection. In addition, this meticulously examines methodological improvements, performance analysis metrics, false-positive deduction techniques, and data usage trends reported in current research. To continue, this review paper investigates persistent limitations, including data imbalance, cross-modality generalization, computational complexity, and limited clinical interpretability