2026/07/01 by Jafar Majidpour, Hunar Abubakir Ahmed, Sayna Jamaati
Computer Science · Medicine · #Advanced Image Processing Techniques #Effects of Radiation Exposure #Lung Cancer Diagnosis and Treatment
paper · doi:10.1002/ima.70417
openalex publication_date 2026/07/01 · openalex created_date 2026/07/04 · openalex updated_date 2026/07/04
ABSTRACT Lung cancer, the largest cause of cancer deaths globally, requires effective diagnostic imaging to identify and define pulmonary abnormalities. Sparse view projections lower CT radiation but diminish image quality and make diagnosis challenging. This research introduces a comprehensive approach that systematically assesses seven sparse projection levels (10–512 views) for lung cancer detection, uniquely integrating generative adversarial networks (GANs)‐based reconstruction with diagnostic performance evaluation. In contrast to previous research, our study offers a systematic framework for generating and assessing sparse‐view datasets over the entire clinical range. Given the limited availability of sparse‐view projection datasets across clinical ranges, we systematically generated sparse‐view images at multiple levels: 10, 16, 32, 64, 128, 256, and 512 projections. Three GAN architectures, standard GAN, conditional GAN (CGAN), and Pix2Pix, were employed to reconstruct high‐quality images from degraded sparse‐view data, enhancing structural integrity and diagnostic utility. A Visual Geometry Group 16‐layer (VGG16) deep learning (DL) model evaluated the diagnostic efficacy of both generated and original sparse‐view images. Results demonstrate superior performance of the Pix2Pix model, achieving structural similarity index measure values of 79.385% and 81.265% for 10‐ and 16‐view projections, respectively. Classification performance using VGG16 yielded exceptional metrics: 98.82% accuracy, 99.18% precision, 99.18% recall, 99.18% F1‐score, and 99.86% area under the receiver operating characteristic curve for 10‐view projections. This GAN‐DL integration offers a clinically viable approach for sparse‐view imaging, ensuring diagnostic reliability while minimizing radiation exposure and enhancing computational efficiency, particularly critical for cancer patients requiring reduced radiation doses.