The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries....

Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propose a deep artificial neural network architecture, ReCTnet, for the fully-auto...

This study uses a revolutionary image recognition method, deep learning, for the classification of potentially malignant pulmonary nodules. Deep learning is based on deep neural networks. We report results of our initial findings and compare performance of deep neural nets using a combination of different network topologies and optimization parameters. Classification accuracy, sensitivity and s...

Research studies have shown that advances in computed tomography (CT) technology allow better detection of pulmonary nodules by generating higher-resolution images. However, the new technology also generates many more individual transversal reconstructions, which as a result may affect the efficiency and accuracy of the radiologists interpreting these images. The goal of our research study is t...

OBJECTIVES The pulmonary nodule is an important diagnostic and therapeutic problem. Diagnostic certainty is only obtained by histological examination. Mini-invasive surgery allows removal of the nodule with minimal sequelae for the patient. METHODS From October 1991 to December 2006, 370 resections for a pulmonary nodule were performed at our Department of General Surgery of the University of...

In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection diagnosis nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired diagnostic process ...