Comparison of back-propagation ANN and Naïve-Bayes method in identifying lung X-ray images

M. Arief Bustomi

doi:10.1063/5.0072373

Comparison of back-propagation ANN and Naïve-Bayes method in identifying lung X-ray images

M. Arief Bustomi^*

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This paper presents the results of a comparison of two methods for the identification of X-ray images of the lungs, namely the ANN back-propagation method and the Nai"ve-Bayes method. The ANN back-propagation method works based on the final weighting of each network as a result of the identification training process. The Nai"ve-Bayes method works based on the probabilities of each characteristic of the identification training process. The process of identifying X-ray images of the lungs is carried out based on the extraction of histogram features on X-ray images of the lungs to be identified into three groups, namely X-ray images of normal lungs, X-ray images of lung cancer, and an X- ray image of the lung affected by effusion. This study aims to compare the strengths or weaknesses between the weight- based ANN back-propagation method and the probability-based Nai"ve-Bayes method in the ability to identify lung X-ray images. The results of this study indicate that the Nai"ve-Bayes method is superior to ANN back-propagation in identifying X-ray images of lungs.

Original language	English
Title of host publication	International Conference on Science and Applied Science, ICSAS 2021
Editors	Budi Purnama, Dewanta Arya Nugraha, A. Suparmi
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735441859
DOIs	https://doi.org/10.1063/5.0072373
Publication status	Published - 24 Mar 2022
Event	2021 International Conference on Science and Applied Science, ICSAS 2021 - Surakarta, Virtual, Indonesia Duration: 6 Apr 2021 → 6 Apr 2021

Publication series

Name	AIP Conference Proceedings
Volume	2391
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2021 International Conference on Science and Applied Science, ICSAS 2021
Country/Territory	Indonesia
City	Surakarta, Virtual
Period	6/04/21 → 6/04/21

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1063/5.0072373

Cite this

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title = "Comparison of back-propagation ANN and Na{\"i}ve-Bayes method in identifying lung X-ray images",

abstract = "This paper presents the results of a comparison of two methods for the identification of X-ray images of the lungs, namely the ANN back-propagation method and the Nai{"}ve-Bayes method. The ANN back-propagation method works based on the final weighting of each network as a result of the identification training process. The Nai{"}ve-Bayes method works based on the probabilities of each characteristic of the identification training process. The process of identifying X-ray images of the lungs is carried out based on the extraction of histogram features on X-ray images of the lungs to be identified into three groups, namely X-ray images of normal lungs, X-ray images of lung cancer, and an X- ray image of the lung affected by effusion. This study aims to compare the strengths or weaknesses between the weight- based ANN back-propagation method and the probability-based Nai{"}ve-Bayes method in the ability to identify lung X-ray images. The results of this study indicate that the Nai{"}ve-Bayes method is superior to ANN back-propagation in identifying X-ray images of lungs.",

author = "Bustomi, \{M. Arief\}",

note = "Publisher Copyright: {\textcopyright} 2022 Author(s).; 2021 International Conference on Science and Applied Science, ICSAS 2021 ; Conference date: 06-04-2021 Through 06-04-2021",

year = "2022",

month = mar,

day = "24",

doi = "10.1063/5.0072373",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Budi Purnama and Nugraha, \{Dewanta Arya\} and A. Suparmi",

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Bustomi, MA 2022, Comparison of back-propagation ANN and Naïve-Bayes method in identifying lung X-ray images. in B Purnama, DA Nugraha & A Suparmi (eds), International Conference on Science and Applied Science, ICSAS 2021., 020016, AIP Conference Proceedings, vol. 2391, American Institute of Physics Inc., 2021 International Conference on Science and Applied Science, ICSAS 2021, Surakarta, Virtual, Indonesia, 6/04/21. https://doi.org/10.1063/5.0072373

Comparison of back-propagation ANN and Naïve-Bayes method in identifying lung X-ray images. / Bustomi, M. Arief.
International Conference on Science and Applied Science, ICSAS 2021. ed. / Budi Purnama; Dewanta Arya Nugraha; A. Suparmi. American Institute of Physics Inc., 2022. 020016 (AIP Conference Proceedings; Vol. 2391).

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

TY - GEN

T1 - Comparison of back-propagation ANN and Naïve-Bayes method in identifying lung X-ray images

AU - Bustomi, M. Arief

PY - 2022/3/24

Y1 - 2022/3/24

N2 - This paper presents the results of a comparison of two methods for the identification of X-ray images of the lungs, namely the ANN back-propagation method and the Nai"ve-Bayes method. The ANN back-propagation method works based on the final weighting of each network as a result of the identification training process. The Nai"ve-Bayes method works based on the probabilities of each characteristic of the identification training process. The process of identifying X-ray images of the lungs is carried out based on the extraction of histogram features on X-ray images of the lungs to be identified into three groups, namely X-ray images of normal lungs, X-ray images of lung cancer, and an X- ray image of the lung affected by effusion. This study aims to compare the strengths or weaknesses between the weight- based ANN back-propagation method and the probability-based Nai"ve-Bayes method in the ability to identify lung X-ray images. The results of this study indicate that the Nai"ve-Bayes method is superior to ANN back-propagation in identifying X-ray images of lungs.

AB - This paper presents the results of a comparison of two methods for the identification of X-ray images of the lungs, namely the ANN back-propagation method and the Nai"ve-Bayes method. The ANN back-propagation method works based on the final weighting of each network as a result of the identification training process. The Nai"ve-Bayes method works based on the probabilities of each characteristic of the identification training process. The process of identifying X-ray images of the lungs is carried out based on the extraction of histogram features on X-ray images of the lungs to be identified into three groups, namely X-ray images of normal lungs, X-ray images of lung cancer, and an X- ray image of the lung affected by effusion. This study aims to compare the strengths or weaknesses between the weight- based ANN back-propagation method and the probability-based Nai"ve-Bayes method in the ability to identify lung X-ray images. The results of this study indicate that the Nai"ve-Bayes method is superior to ANN back-propagation in identifying X-ray images of lungs.

UR - https://www.scopus.com/pages/publications/85127954822

U2 - 10.1063/5.0072373

DO - 10.1063/5.0072373

M3 - Conference contribution

AN - SCOPUS:85127954822

T3 - AIP Conference Proceedings

BT - International Conference on Science and Applied Science, ICSAS 2021

A2 - Purnama, Budi

A2 - Nugraha, Dewanta Arya

A2 - Suparmi, A.

PB - American Institute of Physics Inc.

T2 - 2021 International Conference on Science and Applied Science, ICSAS 2021

Y2 - 6 April 2021 through 6 April 2021

ER -

Comparison of back-propagation ANN and Naïve-Bayes method in identifying lung X-ray images

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