There have been major developments in deep learning in computer vision since the 2010s.
Deep learning has contributed to a wealth of data in medical image processing, and …

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

Lung nodule segmentation plays a crucial role in early-stage lung cancer diagnosis, and
early detection of lung cancer can improve the survival rate of the patients. The approaches …

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 …

Lung cancer screening using computed tomography (CT) has increased the detection rate of
small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically …

Background and objective Lung cancer counts among diseases with the highest global
morbidity and mortality rates. The automatic segmentation of lung tumors from CT images is …

Background Ultrasonography—an imaging technique that can show the anatomical section
of nerves and surrounding tissues—is one of the most effective imaging methods to …

Precise and unambiguous segmentation of pulmonary nodules from the CT images is
imperative for a CAD framework implementation delineated for the prognosis of lung cancer …

Advancements in the development of computer-aided decision (CAD) systems for clinical
routines provide unquestionable benefits in connecting human medical expertise with …

Lung cancer has the highest mortality rate. Its diagnosis and treatment analysis depends
upon the accurate segmentation of the tumor. It becomes tedious if done manually as …