Next Article in Journal
Polarization-Charge Inversion at Al2O3/GaN Interfaces through Post-Deposition Annealing
Previous Article in Journal
Three-Dimensional Time-Harmonic Electromagnetic Scattering Problems from Bianisotropic Materials and Metamaterials: Reference Solutions Provided by Converging Finite Element Approximations
Previous Article in Special Issue
A Segmentation Enhancement Method for the Low-Contrast and Narrow-Banded Substances in CBCT Images

This is an early access version, the complete PDF, HTML, and XML versions will be available soon.

Open AccessArticle

Lung Nodule Classification Using Taguchi-Based Convolutional Neural Networks for Computer Tomography Images

by Cheng-Jian Lin 1,2,* and Yu-Chi Li 1
1
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan
2
College of Intelligence, National Taichung University of Science and Technology, Taichung 404, Taiwan
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(7), 1066; https://doi.org/10.3390/electronics9071066
Received: 3 June 2020 / Revised: 17 June 2020 / Accepted: 29 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Application of Electronic Devices on Intelligent System)
Lung cancer occurs in the lungs, trachea, or bronchi. This cancer is often caused by malignant nodules. These cancer cells spread uncontrollably to other organs of the body and pose a threat to life. An accurate assessment of disease severity is critical to determining the optimal treatment approach. In this study, a Taguchi-based convolutional neural network (CNN) was proposed for classifying nodules into malignant or benign. For setting parameters in a CNN, most users adopt trial and error to determine structural parameters. This study used the Taguchi method for selecting preliminary factors. The orthogonal table design is used in the Taguchi method. The final optimal parameter combination was determined, as were the most significant parameters. To verify the proposed method, the lung image database consortium data set from the National Cancer Institute was used for analysis. The database contains a total of 16,471 images, including 11,139 malignant nodule images. The experimental results demonstrated that the proposed method with the optimal parameter combination obtained an accuracy of 99.6%.
Keywords: lung cancer; convolutional neural networks; Taguchi method; computer tomography image lung cancer; convolutional neural networks; Taguchi method; computer tomography image
MDPI and ACS Style

Lin, C.-J.; Li, Y.-C. Lung Nodule Classification Using Taguchi-Based Convolutional Neural Networks for Computer Tomography Images. Electronics 2020, 9, 1066.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop