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http://dx.doi.org/10.9717/kmms.2020.23.6.729

Performance Comparison of Commercial and Customized CNN for Detection in Nodular Lung Cancer  

Park, Sung-Wook (Dept. of Computer Engineering, Sunchon National University)
Kim, Seunghyun (Dept. of Computer Engineering, Sunchon National University)
Lim, Su-Chang (Dept. of Computer Engineering, Sunchon National University)
Kim, Do-Yeon (Dept. of Computer Engineering, Sunchon National University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.6, 2020 , pp. 729-737 More about this Journal
Abstract
Screening with low-dose spiral computed tomography (LDCT) has been shown to reduce lung cancer mortality by about 20% when compared to standard chest radiography. One of the problems arising from screening programs is that large amounts of CT image data must be interpreted by radiologists. To solve this problem, automated detection of pulmonary nodules is necessary; however, this is a challenging task because of the high number of false positive results. Here we demonstrate detection of pulmonary nodules using six off-the-shelf convolutional neural network (CNN) models after modification of the input/output layers and end-to-end training based on publicly databases for comparative evaluation. We used the well-known CNN models, LeNet-5, VGG-16, GoogLeNet Inception V3, ResNet-152, DensNet-201, and NASNet. Most of the CNN models provided superior results to those of obtained using customized CNN models. It is more desirable to modify the proven off-the-shelf network model than to customize the network model to detect the pulmonary nodules.
Keywords
Pulmonary Nodule; Computer Aided Detection; Deep Neural Network; Convolutional Neural Network;
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