Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-
consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) …

This paper presents a systematic review of the literature focused on the lung nodule
detection in chest computed tomography (CT) images. Manual detection of lung nodules by …

In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …

Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the
pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic …

Pulmonary cancer is considered as one of the major causes of death worldwide. For the
detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed …

Classification of benign–malignant lung nodules on chest CT is the most critical step in the
early detection of lung cancer and prolongation of patient survival. Despite their success in …

The spatial heterogeneity is an important indicator of the malignancy of lung nodules in lung
cancer diagnosis. Compared with 2D nodule CT images, the 3D volumes with entire nodule …

Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers.
Recently, deep learning techniques have enabled remarkable progress in this field …

Background Accurate segmentation and recognition algorithm of lung nodules has great
important value of reference for early diagnosis of lung cancer. An algorithm is proposed for …

Classification of benign and malignant in lung nodules using chest CT images is a key step
in the diagnosis of early-stage lung cancer, as well as an effective way to improve the …