Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

International Journal of Advances in Intelligent Informatics

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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
Date 2019-11-17
Type info:eu-repo/semantics/article

Format application/pdf
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 256-272
Language eng

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