Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks

• Published in: International journal for computer assisted radiology and surgery Vol. 15; no. 1; pp. 173 - 178
• Main Authors: Onishi, Yuya, Teramoto, Atsushi, Tsujimoto, Masakazu, Tsukamoto, Tetsuya, Saito, Kuniaki, Toyama, Hiroshi, Imaizumi, Kazuyoshi, Fujita, Hiroshi
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.01.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Classification; Computed tomography; Computer Imaging; Computer Science; Cross-sections; Generative adversarial networks; Health Informatics; Image classification; Image enhancement; Imaging; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Nodules; Pattern Recognition and Graphics; Radiology; Sensitivity analysis; Short Communication; Surgery; Vision; DCNN; CT imaging; Multiplanar analysis; GAN; Pulmonary nodule; Classification

Purpose Early detection and treatment of lung cancer holds great importance. However, pulmonary-nodule classification using CT images alone is difficult to realize. To address this concern, a method for pulmonary-nodule classification based on a deep convolutional neural network (DCNN) and generativ...