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

• Main Authors: Cheng-Jian Lin, Yu-Chi Li
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Electronics
• Subjects: lung cancer; convolutional neural networks; Taguchi method; computer tomography image
• Online Access: https://www.mdpi.com/2079-9292/9/7/1066

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%.