Computer-Aided Detection of Lung Cancer

Abstract

As medical imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Consequently, computer aids are becoming indispensable in physicians’ decision-making based on medical images. Computer-aided diagnosis (CAD) has been investigated and becomes an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a “second opinion.” In CAD research, detection of lung cancer in thoracic imaging constitutes a major research area, because lung cancer is the leading cause of cancer death worldwide, including the United States, Japan, and other countries. In this chapter, CAD for the detection of lung cancer in thoracic computed tomography (CT) is overviewed with emphasis on machine learning that plays an essential role in CAD systems. Massive training artificial neural network (MTANN) technology is one of the most promising machine learning techniques in image analysis. The MTANNs have substantially improved the sensitivity and specificity of CAD systems in detection and diagnosis of lung cancer. MTANN CAD systems offer high performance in detection and diagnosis of lung cancer in CT. Thus, MTANN CAD systems would be useful for improving the diagnostic performance of radiologists/physicians in early detection of lung cancer.