This paper overviews one of the most important, interesting, and challenging problems in
oncology, the problem of lung cancer diagnosis. Develo** an effective computer-aided …

In healthcare, integration of artificial intelligence (AI) holds strong promise for facilitating
clinicians' work, especially in clinical imaging. We aimed to assess the impact of AI …

Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has
been an active area of research for the last two decades. However, there have only been …

Subsolid pulmonary nodules occur less often than solid pulmonary nodules, but show a
much higher malignancy rate. Therefore, accurate detection of this type of pulmonary …

A scheme for the automatic detection of nodules in thoracic computed tomography scans is
presented and extensively evaluated. The algorithm uses the local image features of shape …

Computed tomography (CT) screening is an effective way for early detection of lung cancer
in order to improve the survival rate of such a deadly disease. For more than two decades …

Numerous publications and commercial systems are available that deal with automatic
detection of pulmonary nodules in thoracic computed tomography scans, but a comparative …

Computer-aided detection (CAD) can help radiologists to detect pulmonary nodules at an
early stage. In pulmonary nodule CAD systems, feature extraction is very important for …

Purpose: The paper presents a complete computer‐aided detection (CAD) system for the
detection of lung nodules in computed tomography images. A new mixedfeature selection …

Purpose: M5L, a fully automated computer‐aided detection (CAD) system for the detection
and segmentation of lung nodules in thoracic computed tomography (CT), is presented and …