Proceedings of the International Conference on Science and Technology (ICST 2018)

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Authors
I Wayan Budi Sentana, Sri Andriati Asri, Naser Jawas, Anggun Esti Wardani
Corresponding Author
I Wayan Budi Sentana
Available Online December 2018.
DOI
https://doi.org/10.2991/icst-18.2018.7How to use a DOI?
Keywords
CNN, SVM, Lung Nodule, Computed Tomography Images
Abstract
Convolutional Neural Networks (CNN) are biologically-inspired variants of Multiple Layer Perceptron (MLPs). Meanwhile, support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. In some research related to image processing, each algorithm has its owned supremacy as well as the drawback. None of the previous research compare both algorithm when they are utilized to detect lung nodule in Computed Tomography (CT) images. Hence, this research comparing the two algorithms in case of lung nodule detection in CT images, since detecting lung nodule in CT images is still challenging. SVM-based classifier is preceded by feature extraction as its common behavior of mathematical based classifier. There are three algorithms use to conduct feature extraction process, namely Hu moment invariant, Haralick and Color Histogram extraction. In the opposite, CNN-based classifier consists of three layers convolution for training and testing steps. The result shows that SVM has better results than CNN in case of computing speed. Meanwhile have a better accuracy in detecting lung nodule. The results of the test analysis show that the extractor feature when preprocessing conduct before being classified by SVM makes the computing process faster. The accuracy of SVM-based classifier can be improved by adjusting some computation variables in feature extraction stages, such as adding more bins in the color histogram extraction. Those adjustment will lead to more computation times.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Science and Technology (ICST 2018)
Series
Atlantis Highlights in Engineering
Publication Date
December 2018
ISBN
978-94-6252-650-1
ISSN
2589-4943
DOI
https://doi.org/10.2991/icst-18.2018.7How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - I Wayan Budi Sentana
AU  - Sri Andriati Asri
AU  - Naser Jawas
AU  - Anggun Esti Wardani
PY  - 2018/12
DA  - 2018/12
TI  - CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images
BT  - Proceedings of the International Conference on Science and Technology (ICST 2018)
PB  - Atlantis Press
SP  - 29
EP  - 34
SN  - 2589-4943
UR  - https://doi.org/10.2991/icst-18.2018.7
DO  - https://doi.org/10.2991/icst-18.2018.7
ID  - Sentana2018/12
ER  -