Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

• Published in: BioMed research international Vol. 2019; no. 2019; pp. 1 - 9
• Main Authors: Fujita, Hiroshi, Toyama, Hiroshi, Saito, Kuniaki, Tsukamoto, Tetsuya, Tsujimoto, Masakazu, Teramoto, Atsushi, Onishi, Yuya, Imaizumi, Kazuyoshi
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial neural networks; Automation; Benign; Biomedical research; Biopsy; Cancer; Classification; Computation; Computed tomography; Diagnosis; Female; Generative adversarial networks; Humans; Image classification; Invasiveness; Lung - diagnostic imaging; Lung - pathology; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lung nodules; Male; Malignancy; Medical imaging; Medical research; Neural networks; Neural Networks, Computer; Nodules; Radiographic Image Interpretation, Computer-Assisted - methods; Solitary Pulmonary Nodule - diagnostic imaging; Solitary Pulmonary Nodule - pathology; Tomography; Tomography, X-Ray Computed - methods; X-rays

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be...