Automatic and accurate segmentation of pulmonary nodules in CT images can help
physicians perform more accurate quantitative analysis, diagnose diseases, and improve …

Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis.
Therefore, in this paper, we propose a deep learning method using a transfer learning …

Lung cancer is the most common cause of cancer deaths worldwide. Early detection is
crucial for successful treatment and increasing patient survival rates. Artificial intelligence …

Accurate segmentation of lesions in medical images is a key step to assist clinicians in
diagnosis and analysis. Most studies combine the Transformer model with CNN at a single …

Accurate segmenting regions of interest in various medical images are essential to clinical
research and applications. Although deep learning-based methods have achieved good …

Lung tumor segmentation in PET-CT images plays an important role to assist physicians in
clinical application to accurately diagnose and treat lung cancer. However, it is still a …

Automatic lung infection segmentation in computed tomography (CT) scans can offer great
assistance in radiological diagnosis by improving accuracy and reducing time required for …

Abdominal tumor segmentation is a crucial yet challenging step during the screening and
diagnosis of tumors. While 3D segmentation models provide powerful performance, they …

Hepatocellular carcinoma, representing the most frequent primary liver cancer, is a common
cancer disease that is the fourth leading cause of cancer-related mortality worldwide. In …

In leather images, precise pore segmentation is a necessary medium for accurate species
prediction. However, due to foreground color, texture, size, and boundary variability …