Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, developing non-invasive systems to classify lung cancer histological …

Lung cancer has become a prevalent form of cancer; it can be found in persons of all age
groups. The early stage identification of lung cancer is required to control the integrated …

Accurate and timely detection and classification of lung abnormalities are crucial for effective
diagnosis and treatment planning. In recent years, Deep Learning (DL) techniques have …

The computer-aided diagnosis (CAD) method plays a considerable role in the automated
recognition of medical images, considering the increasing numbers of lung cancer patients …

At present, researchers have been try to enhance the CAD system performance utilizing
deep learning techniques in lung cancer screening and computed tomography (CT), but …

Malignant pulmonary nodules must be identified promptly to improve the life chances of lung
disorder patients. Lung cancer is the most severe type of cancer, and early detection directly …

In this study, we propose a novel approach called DCNN-ViT-GRU, which combines deep
Convolutional Neural Networks (CNNs) with Gated Recurrent Units (GRUs) and the Vision …

Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early
on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis …

The manifestations of early lung cancer in medical imaging often appear as pulmonary
nodules, which can be classified as benign or malignant. In recent years, there has been a …

Lung cancer is often a fatal disease. To minimize patient mortality, the ability to identify the
nodule malignancy stage from computed tomography (CT) lung scans is critical. Most …