Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Improving stroke diagnosis accuracy using hyperparameter optimized deep learning
 
Creator Badriyah, Tessy
Santoso, Dimas Bagus
Syarif, Iwan
Syarif, Daisy Rahmania
 
Subject Feature Selection; Deep Learning; Hyperparameter Optimization
 
Description Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2019-11-17
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/427
10.26555/ijain.v5i3.427
 
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 256-272
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/427/ijain_v5i3_p256-272
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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