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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C. and Fei-Fei, L. (2015) ImageNet Large Scale Visual Recognition Challenge. Int. J. Comp. Vis., 115, 211-252. https://doi.org/10.1007/s11263-015-0816-y

has been cited by the following article:

• TITLE: A Study on Diagnostic Assist Systems of Chronic Obstructive Pulmonary Disease from Medical Images by Deep Learning

  AUTHORS: Toru Kimura, Takashi Kawakami, Akihiro Kikuchi, Ryosuke Ooev, Masaki Akiyama, Hiroyuki Horikoshi

  KEYWORDS: Deep Learning, CT Images, Diagnostic Assist Systems, Chronic Obstructive Pulmonary Disease

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.1, December 29, 2017

  ABSTRACT: In this paper, we propose new diagnostic assist systems of medical images using deep learning algorithms. Specifically, we aim to develop a diagnostic support system for the very early stage of chronic obstructive pulmonary disease (COPD) based on the CT images. It is said that COPD is a disease that develops due to long-term smoking, and it is said that there are a large number of latent onset reserve forces. By discovering this COPD in the very early period 0 and improving the living conditions, subsequent severity can be avoided in many cases, so a system that will help diagnosis by professional radiologists is needed. We show the some experimental results examined by the constructed system.