3d-cnn based computer-aided diagnosis (cadx) for lung nodule diagnosis

Tzu Chi Tai, Miao Tian, Wei Ting Cho, Chin Feng Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution


For the lung nodule screening, one of the commonly testing methods is the chest radiograph. However, it is difficult to judge with the naked eye with the initial nodule size is usually less than one centimeter. It is known that skilled pulmonary radiologists have a high degree of accuracy in diagnosis, but there remain problems in disease diagnosis. These problems include the miss rate for diagnosis of small nodules and the diagnosis of change in preexisting interstitial lung disease. The recent studies have found that 68% lung cancer nodules in radiographs can be detected by one reader and 82% by two readers. In order to solve this problem, we proposed a 3D-CNN predicting model to differ malignant nodules from all nodules in computed tomography scan. In the experiment results, the model was able to achieve a training accuracy of 100% and a testing accuracy of 94.52%. It shows the proposed model is able to be used for improving the accuracy of detecting nodules.

Original languageEnglish
Title of host publicationCognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
EditorsJian Shen, Yao-Chung Chang, Yu-Sheng Su, Hiroaki Ogata
Number of pages9
ISBN (Print)9789811561122
Publication statusPublished - 2020
Event2nd International Cognitive Cities Conference, IC3 2019 - Kyoto, Japan
Duration: 2019 Sept 32019 Sept 6

Publication series

NameCommunications in Computer and Information Science
Volume1227 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference2nd International Cognitive Cities Conference, IC3 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)


Dive into the research topics of '3d-cnn based computer-aided diagnosis (cadx) for lung nodule diagnosis'. Together they form a unique fingerprint.

Cite this