Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2018 Jul 27;13(7):e0200721.
doi: 10.1371/journal.pone.0200721. eCollection 2018.

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Mizuho Nishio  1   2 , Osamu Sugiyama  2 , Masahiro Yakami  1   2 , Syoko Ueno  3 , Takeshi Kubo  1 , Tomohiro Kuroda  4 , Kaori Togashi  1
Affiliations
Comparative Study

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

Mizuho Nishio et al. PLoS One. .

Abstract

We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schematic illustration of the modified VGG-16.
Note: Except softmax layer, activation function is not shown.
Fig 2
Fig 2. Representative CT images of lung nodules.
(A) benign nodule, (B) primary lung cancer and (C) metastatic lung cancer.
Fig 3
Fig 3. Three CT images obtained from three orthogonal planes used for input to 2D-DCNN.
Fig 2(B) is identical to Fig 3(A). (A) axial image, (B) coronal image and (C) sagittal image. Abbreviations: DCNN, deep convolutional neural network.
Fig 4
Fig 4. Representative results of loss and accuracy during DCNN training with transfer learning.
Abbreviations: DCNN, deep convolutional neural network.
Fig 5
Fig 5. Representative results of loss and accuracy during DCNN training without transfer learning.
Abbreviations: DCNN, deep convolutional neural network.
See this image and copyright information in PMC

References

    1. Suzuki K. Computer-Aided Detection of Lung Cancer In: Image-Based Computer-Assisted Radiation Therapy. Singapore: Springer; Singapore; 2017:9–40. 10.1007/978-981-10-2945-5_2 - DOI
    1. El-Baz A, Beache GM, Gimel’farb G, Suzuki K, Okada K, Elnakib A, et al. Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imaging. 2013;2013:942353 10.1155/2013/942353 - DOI - PMC - PubMed
    1. Nishio M, Nagashima C. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. Acad Radiol. 2017;24(3):328–336. 10.1016/j.acra.2016.11.007 - DOI - PubMed
    1. Kawagishi M, Chen B, Furukawa D, Sekiguchi H, Sakai K, Kubo T, et al. A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists. Int J Comput Assist Radiol Surg. 2017;12(5):767–776. 10.1007/s11548-017-1554-0 - DOI - PubMed
    1. de Oseas Carvalho Filho A, Corrêa Silva A, de Cardoso Paiva A, Acatauas Nunes R, Gattass M, Acatauassú Nunes R. Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM. J Digit Imaging. 2017. 10.1007/s10278-017-9973-6 - DOI - PMC - PubMed

Publication types