Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images

Dewinda Julianensi Rumala; Eko Mulyanto Yuniarno; Reza Fuad Rachmadi; Supeno Mardi Susiki Nugroho; Hapsari Peni Agustin Tjahyaningtijas; Yudhi Adrianto; Anggraini Dwi Sensusiati; I. Ketut Eddy Purnama

doi:10.1109/CENIM51130.2020.9297862

Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images

Dewinda Julianensi Rumala, Eko Mulyanto Yuniarno, Reza Fuad Rachmadi, Supeno Mardi Susiki Nugroho, Hapsari Peni Agustin Tjahyaningtijas, Yudhi Adrianto, Anggraini Dwi Sensusiati, I. Ketut Eddy Purnama

Research Center for Artificial Intelligence and Health Technology (KATK)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Early detection and treatment of brain disease are essential. However, brain disease diagnosis used to be challenging, on the other hand imaging techniques such as MRI make it easier. For the past years, many researchers have used several methods of Machine Learning and Deep Learning to diagnose brain abnormalities without any human help. Convolutional Neural Network is the best method to extract features of images automatically. In this study, a Deep Learning model of Convolutional Neural Network algorithm is applied to classify brain MR Images into normal and abnormal classes. The constructed network architecture was evaluated based on several activation functions and numbers of epoch. The experiment results achieved a significant performance with the best accuracy of 99.12% and dice score of 98.17% using ELU activation function at epoch 50. This result indicates that the proposed method has improved the performance of Convolutional Neural Network in brain disease classification.

Original language	English
Title of host publication	CENIM 2020 - Proceeding
Subtitle of host publication	International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	402-407
Number of pages	6
ISBN (Electronic)	9781728182834
DOIs	https://doi.org/10.1109/CENIM51130.2020.9297862
Publication status	Published - 17 Nov 2020
Event	2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia Duration: 17 Nov 2020 → 18 Nov 2020

Publication series

Name	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference	2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/Territory	Indonesia
City	Virtual, Surabaya
Period	17/11/20 → 18/11/20

Keywords

Brain Disease
Convolutional Neural Network
Deep Learning
Image Classification
Medical Image

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CENIM51130.2020.9297862

Cite this

Rumala, D. J., Yuniarno, E. M., Rachmadi, R. F., Nugroho, S. M. S., Tjahyaningtijas, H. P. A., Adrianto, Y., Sensusiati, A. D., & Purnama, I. K. E. (2020). Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images. In CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (pp. 402-407). Article 9297862 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CENIM51130.2020.9297862

Rumala, Dewinda Julianensi ; Yuniarno, Eko Mulyanto ; Rachmadi, Reza Fuad et al. / Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images. CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 402-407 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020).

@inproceedings{3f947c58c8de47129024d4db7b116e79,

title = "Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images",

abstract = "Early detection and treatment of brain disease are essential. However, brain disease diagnosis used to be challenging, on the other hand imaging techniques such as MRI make it easier. For the past years, many researchers have used several methods of Machine Learning and Deep Learning to diagnose brain abnormalities without any human help. Convolutional Neural Network is the best method to extract features of images automatically. In this study, a Deep Learning model of Convolutional Neural Network algorithm is applied to classify brain MR Images into normal and abnormal classes. The constructed network architecture was evaluated based on several activation functions and numbers of epoch. The experiment results achieved a significant performance with the best accuracy of 99.12% and dice score of 98.17% using ELU activation function at epoch 50. This result indicates that the proposed method has improved the performance of Convolutional Neural Network in brain disease classification.",

keywords = "Brain Disease, Convolutional Neural Network, Deep Learning, Image Classification, Medical Image",

author = "Rumala, {Dewinda Julianensi} and Yuniarno, {Eko Mulyanto} and Rachmadi, {Reza Fuad} and Nugroho, {Supeno Mardi Susiki} and Tjahyaningtijas, {Hapsari Peni Agustin} and Yudhi Adrianto and Sensusiati, {Anggraini Dwi} and Purnama, {I. Ketut Eddy}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 ; Conference date: 17-11-2020 Through 18-11-2020",

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doi = "10.1109/CENIM51130.2020.9297862",

language = "English",

series = "CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Rumala, DJ, Yuniarno, EM, Rachmadi, RF , Nugroho, SMS, Tjahyaningtijas, HPA, Adrianto, Y, Sensusiati, AD & Purnama, IKE 2020, Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images. in CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020., 9297862, CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, Institute of Electrical and Electronics Engineers Inc., pp. 402-407, 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020, Virtual, Surabaya, Indonesia, 17/11/20. https://doi.org/10.1109/CENIM51130.2020.9297862

Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images. / Rumala, Dewinda Julianensi; Yuniarno, Eko Mulyanto; Rachmadi, Reza Fuad et al.
CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 402-407 9297862 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images

AU - Rumala, Dewinda Julianensi

AU - Yuniarno, Eko Mulyanto

AU - Rachmadi, Reza Fuad

AU - Nugroho, Supeno Mardi Susiki

AU - Tjahyaningtijas, Hapsari Peni Agustin

AU - Adrianto, Yudhi

AU - Sensusiati, Anggraini Dwi

AU - Purnama, I. Ketut Eddy

PY - 2020/11/17

Y1 - 2020/11/17

N2 - Early detection and treatment of brain disease are essential. However, brain disease diagnosis used to be challenging, on the other hand imaging techniques such as MRI make it easier. For the past years, many researchers have used several methods of Machine Learning and Deep Learning to diagnose brain abnormalities without any human help. Convolutional Neural Network is the best method to extract features of images automatically. In this study, a Deep Learning model of Convolutional Neural Network algorithm is applied to classify brain MR Images into normal and abnormal classes. The constructed network architecture was evaluated based on several activation functions and numbers of epoch. The experiment results achieved a significant performance with the best accuracy of 99.12% and dice score of 98.17% using ELU activation function at epoch 50. This result indicates that the proposed method has improved the performance of Convolutional Neural Network in brain disease classification.

AB - Early detection and treatment of brain disease are essential. However, brain disease diagnosis used to be challenging, on the other hand imaging techniques such as MRI make it easier. For the past years, many researchers have used several methods of Machine Learning and Deep Learning to diagnose brain abnormalities without any human help. Convolutional Neural Network is the best method to extract features of images automatically. In this study, a Deep Learning model of Convolutional Neural Network algorithm is applied to classify brain MR Images into normal and abnormal classes. The constructed network architecture was evaluated based on several activation functions and numbers of epoch. The experiment results achieved a significant performance with the best accuracy of 99.12% and dice score of 98.17% using ELU activation function at epoch 50. This result indicates that the proposed method has improved the performance of Convolutional Neural Network in brain disease classification.

KW - Brain Disease

KW - Convolutional Neural Network

KW - Deep Learning

KW - Image Classification

KW - Medical Image

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U2 - 10.1109/CENIM51130.2020.9297862

DO - 10.1109/CENIM51130.2020.9297862

M3 - Conference contribution

AN - SCOPUS:85099641493

T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

SP - 402

EP - 407

BT - CENIM 2020 - Proceeding

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020

Y2 - 17 November 2020 through 18 November 2020

ER -

Rumala DJ, Yuniarno EM, Rachmadi RF , Nugroho SMS, Tjahyaningtijas HPA, Adrianto Y et al. Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images. In CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 402-407. 9297862. (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020). doi: 10.1109/CENIM51130.2020.9297862

Activation Functions Evaluation to Improve Performance of Convolutional Neural Network in Brain Disease Classification Based on Magnetic Resonance Images

Abstract

Publication series

Conference

Keywords

UN SDGs

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