Analisis Kinerja Algoritma Mesin Pembelajaran untuk Klarifikasi Penyakit Stroke Menggunakan Citra CT Scan

Penulis

Nur Sakinah, Tessy Badriyah, Iwan Syarif

Abstrak

Stroke adalah suatu kondisi dimana pasokan darah ke otak terganggu sehingga bagian tubuh yang dikendalikan oleh area otak yang rusak tidak dapat berfungsi dengan baik. Penyebab stroke antara lain adalah terjadinya penyumbatan pada pembuluh darah (stroke iskemik) atau pecahnya pembuluh darah (stroke hemoragik). Pasien yang terkena stroke harus segera ditangani secepatnya karena sel otak dapat mati dalam hitungan menit. Tindakan penanganan stroke secara cepat dan tepat dapat mengurangi resiko kerusakan otak dan mencegah terjadinya komplikasi. Penelitian ini bertujuan untuk mengembangkan perangkat lunak yang dapat membaca dan menganalisis citra CT scan dari otak, dan kemudian secara otomatis memprediksi apakah citra CT scan tersebut stroke iskemik atau stroke hemoragik. Data citra CT scan berasal dari Rumah Sakit Umum Haji Surabaya yang diambil selama periode Januari-Mei 2019 dan berasal dari 102 pasien yang terindikasi stroke. Sebelum data gambar tersebut diolah dengan menggunakan beberapa algoritma mesin pembelajaran, data tersebut melalui tahap pre-processing yang bertujuan untuk meningkatkan kualitas citra meliputi konversi citra, pemotongan citra, penskalaan, greyscaling, penghilangan noise dan augmentasi. Tahap selanjutnya adalah ekstraksi fitur menggunakan metode Gray-Level Co-Occurrence Matrix (GLCM). Penelitian ini juga bertujuan untuk membandingkan kinerja lima algoritma mesin pembelajaran yaitu Naïve Bayes, Logistic Regression, Neural Network, Support Vector Machine dan Deep Learning yang diterapkan untuk memprediksi penyakit stroke. Hasil percobaan menunjukkan bahwa algoritma Deep Learning menghasilkan tingkat performansi paling tinggi yaitu nilai akurasi 96.78%, presisi 97.59% dan recall 95.92%.

 

Abstract

Stroke is a condition in which the blood supply to the brain is interrupted so that parts of the body that are controlled by damaged brain areas cannot function properly. Causes of strokes include blockages in blood vessels (ischemic stroke) or rupture of blood vessels (hemorrhagic stroke). Stroke patients must be treated as soon as possible because brain cells can die within minutes. The handling of stroke patients quickly can reduce the risk of brain damage and prevent complications. This study aims to develop software that can read and analyze CT scan images from the brain, and then automatically predict whether the CT scan images are ischemic stroke or hemorrhagic stroke. The CT scan image data came from the Surabaya Hajj General Hospital which was taken during the January-May 2019 period and came from 102 patients who had indicated a stroke. Before the image data is processed using several machine learning algorithms, the data goes through a pre-processing phase which aims to improve image quality including image conversion, image cutting, scaling, greyscaling, noise removal and augmentation. The next step is feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM) method. This study also aims to compare the performance of five machine learning algorithms, namely Naïve Bayes, Logistic Regression, Neural Networks, Support Vector Machines and Deep Learning which are applied to predict stroke. The experimental results show that the deep learning algorithm produces the highest level of performance where the accuracy value is 96.78%, 97.59% precision and 95.92% recall.


Teks Lengkap:

PDF

Referensi


The World Stroke Organization, 2016. Global stroke fact sheet, [online] Tersedia di: [diakses 9 Juni 2020].

YUYUN, Y., 2016. Pencitraan Pada Stroke. Malang:Universitas Brawijaya Press.

MARBUN J. T. dkk. 2018. Classification of stroke disease using convolutional neural network. 2nd International Conference on Computing and Applied Informatics 2017, 978 (1), pp.1-6. doi :10.1088/1742-6596/978/1/012092

CHIN C. L. dkk. 2017.An automated early ischemic stroke detection system using CNN deep learning algorithm. 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), pp. 368-372. doi:10.1109/ICAwST.2017.8256481

BADRIYAH T. dkk. 2019a. Improving stroke diagnosis accuracy using hyperparameter optimized deep learning. International Journal of Advances in Intelligent Informatics. 5(3), pp. 256–272.

doi:https://doi.org/10.26555/ijain.v5i3.427

JEENA R. S. AND KUMAR S. 2016. Stroke prediction using SVM. International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 600-602. doi:10.1109/ICCICCT.2016.7988020

SINGH, G. A. P., DAN GUPTA P. K., 2018. Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Computing and Applications Springer London. doi:https://doi.org/10.1007/s00521-018-3518-x

TAHIR, M., BADRIYAH, T. & SYARIF, I. 2018a. Neural Networks Algorithm to Inquire Previous Preeclampsia Factors in Women with Chronic Hypertension During Pregnancy in Childbirth Process. 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC). pp. 51-55. doi: 10.1109/KCIC.2018.8628588

WICAKSONO A. P., BADRIYAH T., & BASUKI A. 2016. Comparison of The Data-Mining Methods in Predicting The Risk Level of Diabetes. EMITTER International Journal of Engineering Technology. 4 (1), pp. 164–178. doi: https://doi.org/10.24003/emitter.v4i1.119

VIJAYA G. AND SUHASINI A. 2019. An Adaptive Preprocessing of Lung CT Images with Various Filters for Better Enhancement. International Research Journal of Engineering and Technology (IRJET) 7(3) pp. 179–184.

BADRIYAH T. dkk. 2019b. Segmentation Stroke Objects based on CT Scan Image using Thresholding Method. 2019 First International Conference on Smart Technology & Urban Development (STUD), pp.1-6. doi:10.1109/STUD49732.2019.9018825

MIKOŁAJCZYK A. DAN GROCHOWSKI M. 2018. Data augmentation for improving deep learning in image classification problem’, 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 117-122. doi: 10.1109/iiphdw.2018.8388338

MIRZAPOUR F. AND GHASSEMIAN H. 2015. Fast GLCM and Gabor Filters for Texture Classification of Very High Resolution Remote Sensing Images. International Journal Of Information And Communication Technology Research Summer, 7 (3). pp 21- 30.

FAHRUDIN T. M., SYARIF I., & A. R. BARAKBAH. 2017. Data Mining Approach for Breast Cancer Patient Recovery. EMITTER International Journal of Engineering Technology. 5(1), pp. 36–71. https://doi.org/10.24003/emitter.v5i1.190

MUIS I A. DAN AFFANDES, M. 2015. Penerapan Metode Support Vector Machine ( SVM ) Menggunakan Kernel Radial Basis Function ( RBF ) Pada Klasifikasi Tweet. Jurnal Sains, Teknologi dan Industri, 12(2), pp. 189–197

VANITHA, C. D. A, DEVARAJ, D. AND VENKATESULU M. 2014. Gene expression data classification using Support Vanitha, C. D. A, Devaraj, D. and Venkatesulu M. (2014) 'Gene expression data classification using Support Vector Machine and mutual information-based gene selection. ELSIVER Procedia Computer Science, 47(C), pp. 13–21.

SRIVASTAVA, D. K. DAN BHAMBHU, L. 2010. Data classification using support vector machine. Journal of Theoretical and Applied Information Technology, 12 (1), pp. 1–7.

LECUN, Y., BENGIO, Y. & HINTON, G. 2015. Deep learning. Nature. 521, 436–444 https://doi.org/10.1038/nature14539

TAHIR, M., BADRIYAH, T. & SYARIF, I. 2018b. Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization. EMITTER International Journal of Engineering Technology, 6. (2). pp. 236–253. doi: https://doi.org/10.24003/emitter.v6i2.287




DOI: http://dx.doi.org/10.25126/jtiik.2020743482