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Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks

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@article{IJASEIT13000,
   author = {Glori Stephani Saragih and Zuherman Rustam and Dipo Aldila and Rahmat Hidayat and Reyhan E. Yunus and Jacub Pandelaki},
   title = {Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {2177--2182},
   keywords = {convolutional neural networks; image classification; neuroimaging; random forests; stroke ischemic.},
   abstract = {Stroke has become a global health problem, due to high mortality and disability, with two-thirds of all strokes occurring in developing countries. In Indonesia, stroke is a disease with the highest mortality rate, namely in the first rank for more than two decades, 1990-2017. Stroke is divided into two types, ischemic and hemorrhagic; however, 87% of stroke sufferers are ischemic stroke. Suppose an ischemic stroke is found, and the patient is a new sufferer. In that case, the patient should get direct treatment because there is a golden period in stroke management that is if 4.5 hours to help and reduce the risk of death or permanent disability. High mortality and disability raise awareness of the importance of early detection of ischemic stroke; therefore, research has been carried out, especially in technology. To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results. In this study, the authors will provide an update in the detection of ischemic stroke based on patient CT scan by replacing NN's role on CNN with random forests. Thus, after feature extraction on CNN, the fully connected layer on CNN is completely replaced by random forests in classifying data. Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.},
   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=13000},
   doi = {10.18517/ijaseit.10.5.13000}
}

EndNote

%A Saragih, Glori Stephani
%A Rustam, Zuherman
%A Aldila, Dipo
%A Hidayat, Rahmat
%A Yunus, Reyhan E.
%A Pandelaki, Jacub
%D 2020
%T Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks
%B 2020
%9 convolutional neural networks; image classification; neuroimaging; random forests; stroke ischemic.
%! Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks
%K convolutional neural networks; image classification; neuroimaging; random forests; stroke ischemic.
%X Stroke has become a global health problem, due to high mortality and disability, with two-thirds of all strokes occurring in developing countries. In Indonesia, stroke is a disease with the highest mortality rate, namely in the first rank for more than two decades, 1990-2017. Stroke is divided into two types, ischemic and hemorrhagic; however, 87% of stroke sufferers are ischemic stroke. Suppose an ischemic stroke is found, and the patient is a new sufferer. In that case, the patient should get direct treatment because there is a golden period in stroke management that is if 4.5 hours to help and reduce the risk of death or permanent disability. High mortality and disability raise awareness of the importance of early detection of ischemic stroke; therefore, research has been carried out, especially in technology. To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results. In this study, the authors will provide an update in the detection of ischemic stroke based on patient CT scan by replacing NN's role on CNN with random forests. Thus, after feature extraction on CNN, the fully connected layer on CNN is completely replaced by random forests in classifying data. Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13000
%R doi:10.18517/ijaseit.10.5.13000
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 5
%@ 2088-5334

IEEE

Glori Stephani Saragih,Zuherman Rustam,Dipo Aldila,Rahmat Hidayat,Reyhan E. Yunus and Jacub Pandelaki,"Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 2177-2182, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.13000.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Saragih, Glori Stephani
AU  - Rustam, Zuherman
AU  - Aldila, Dipo
AU  - Hidayat, Rahmat
AU  - Yunus, Reyhan E.
AU  - Pandelaki, Jacub
PY  - 2020
TI  - Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 2177
EP  - 2182
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - convolutional neural networks; image classification; neuroimaging; random forests; stroke ischemic.
N2  - Stroke has become a global health problem, due to high mortality and disability, with two-thirds of all strokes occurring in developing countries. In Indonesia, stroke is a disease with the highest mortality rate, namely in the first rank for more than two decades, 1990-2017. Stroke is divided into two types, ischemic and hemorrhagic; however, 87% of stroke sufferers are ischemic stroke. Suppose an ischemic stroke is found, and the patient is a new sufferer. In that case, the patient should get direct treatment because there is a golden period in stroke management that is if 4.5 hours to help and reduce the risk of death or permanent disability. High mortality and disability raise awareness of the importance of early detection of ischemic stroke; therefore, research has been carried out, especially in technology. To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results. In this study, the authors will provide an update in the detection of ischemic stroke based on patient CT scan by replacing NN's role on CNN with random forests. Thus, after feature extraction on CNN, the fully connected layer on CNN is completely replaced by random forests in classifying data. Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13000
DO  - 10.18517/ijaseit.10.5.13000

RefWorks

RT Journal Article
ID 13000
A1 Saragih, Glori Stephani
A1 Rustam, Zuherman
A1 Aldila, Dipo
A1 Hidayat, Rahmat
A1 Yunus, Reyhan E.
A1 Pandelaki, Jacub
T1 Ischemic Stroke Classification using Random Forests Based on Feature Extraction of Convolutional Neural Networks
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 2177
OP 2182
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 convolutional neural networks; image classification; neuroimaging; random forests; stroke ischemic.
AB Stroke has become a global health problem, due to high mortality and disability, with two-thirds of all strokes occurring in developing countries. In Indonesia, stroke is a disease with the highest mortality rate, namely in the first rank for more than two decades, 1990-2017. Stroke is divided into two types, ischemic and hemorrhagic; however, 87% of stroke sufferers are ischemic stroke. Suppose an ischemic stroke is found, and the patient is a new sufferer. In that case, the patient should get direct treatment because there is a golden period in stroke management that is if 4.5 hours to help and reduce the risk of death or permanent disability. High mortality and disability raise awareness of the importance of early detection of ischemic stroke; therefore, research has been carried out, especially in technology. To carry out automatic diagnosis, machine learning and deep learning can be used, especially because of their ability to provide high accuracy prediction results. In this study, the authors will provide an update in the detection of ischemic stroke based on patient CT scan by replacing NN's role on CNN with random forests. Thus, after feature extraction on CNN, the fully connected layer on CNN is completely replaced by random forests in classifying data. Based on the proposed method, the accuracy of testing is 100% when the percentage of the testing dataset is 10% and the number of trees more than 100 with criterion Gini or entropy.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13000
DO  - 10.18517/ijaseit.10.5.13000