BibTeX
@article{IJASEIT12113,
author = {Zuherman Rustam and Sri Hartini and Rivan Y. Pratama and Reyhan E. Yunus and Rahmat Hidayat},
title = {Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {10},
number = {3},
year = {2020},
pages = {1200--1206},
keywords = {artificial intelligence; artificial neural network; deep learning; image classification; kernel function; k-means clustering; lung cancer diagnosis.},
abstract = {In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold cross-validation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12113},
doi = {10.18517/ijaseit.10.3.12113}
}
EndNote
%A Rustam, Zuherman
%A Hartini, Sri
%A Pratama, Rivan Y.
%A Yunus, Reyhan E.
%A Hidayat, Rahmat
%D 2020
%T Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis
%B 2020
%9 artificial intelligence; artificial neural network; deep learning; image classification; kernel function; k-means clustering; lung cancer diagnosis.
%! Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis
%K artificial intelligence; artificial neural network; deep learning; image classification; kernel function; k-means clustering; lung cancer diagnosis.
%X In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold cross-validation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12113
%R doi:10.18517/ijaseit.10.3.12113
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 3
%@ 2088-5334
IEEE
Zuherman Rustam,Sri Hartini,Rivan Y. Pratama,Reyhan E. Yunus and Rahmat Hidayat,"Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 3, pp. 1200-1206, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.3.12113.
RefMan/ProCite (RIS)
TY - JOUR
AU - Rustam, Zuherman
AU - Hartini, Sri
AU - Pratama, Rivan Y.
AU - Yunus, Reyhan E.
AU - Hidayat, Rahmat
PY - 2020
TI - Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 3
Y2 - 2020
SP - 1200
EP - 1206
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - artificial intelligence; artificial neural network; deep learning; image classification; kernel function; k-means clustering; lung cancer diagnosis.
N2 - In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold cross-validation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12113
DO - 10.18517/ijaseit.10.3.12113
RefWorks
RT Journal Article
ID 12113
A1 Rustam, Zuherman
A1 Hartini, Sri
A1 Pratama, Rivan Y.
A1 Yunus, Reyhan E.
A1 Hidayat, Rahmat
T1 Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 3
YR 2020
SP 1200
OP 1206
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 artificial intelligence; artificial neural network; deep learning; image classification; kernel function; k-means clustering; lung cancer diagnosis.
AB In this paper, we proposed the modified deep learning method that combined Convolutional Neural Network (CNN) and Kernel K-Means clustering for lung cancer diagnosis. The Anti-PD-1 Immunotherapy Lung dataset obtained from The Cancer Imaging Archive was used to evaluate our proposed method. From this dataset, we use 400 Magnetic Resonance Imaging (MRI) images that manually labeled consists of 150 healthy lung images and 250 lung cancer images. As the first step, all the data was examined through the CNN architecture. The flatten neuron of the feature map for every image resulted from the convolutional layers in CNN is gained and passed through the kernel k-means clustering algorithm. This algorithm then used to obtain the centroid of each cluster that determines the prediction class of every data point in the validation set. The performance of our proposed method was evaluated using several k values in k-fold cross-validation. According to our experiments, our proposed method achieved the highest performance measure with 98.85 percent accuracy, 98.32 percent sensitivity, 99.40 percent precision, 99.39 percent specificity, and 98.86 percent F1-Score when using RBF kernel function with sigma=0.05 in 9-fold cross-validation. Those performance improves 1.31% sensitivity, 1.12% accuracy, 1.11% F1-Score, 0.92% specificity, and 0.91% precision compared to when using 5-fold cross-validation. It is even obtained in less than 8 seconds for passing the dataset to the CNN model and 40 ± 0.77 seconds for examined in kernel k-means clustering. Therefore, it was proved that our proposed method has an efficient and promised performance for lung cancer diagnosis from MRI images.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12113
DO - 10.18517/ijaseit.10.3.12113
