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Vol. 6, No. 2, May 2021

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Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

https://doi.org/10.22219/kinetik.v6i2.1219
Agus Eko Minarno
Universitas Muhammadiyah Malang
http://orcid.org/0000-0002-4063-4812
Mochammad Hazmi Cokro Mandiri
Universitas Muhammadiyah Malang
Yuda Munarko
Universitas Muhammadiyah Malang
Hariyady Hariyady
Universitas Muhammadiyah Malang

Corresponding Author(s) : Agus Eko Minarno

aguseko@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 6, No. 2, May 2021

Abstract

Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

Keywords

Brain Tumor MRI Convolutional Neural Network Hyperparameter Tuning
Minarno, A. E., Hazmi Cokro Mandiri, M. ., Munarko, Y. ., & Hariyady, H. (2021). Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(2). https://doi.org/10.22219/kinetik.v6i2.1219
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