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 proceedingConference contributionpeer-review

1 Citation (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 languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2021
EditorsBudi Purnama, Dewanta Arya Nugraha, A. Suparmi
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735441859
DOIs
Publication statusPublished - 24 Mar 2022
Event2021 International Conference on Science and Applied Science, ICSAS 2021 - Surakarta, Virtual, Indonesia
Duration: 6 Apr 20216 Apr 2021

Publication series

NameAIP Conference Proceedings
Volume2391
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2021 International Conference on Science and Applied Science, ICSAS 2021
Country/TerritoryIndonesia
CitySurakarta, Virtual
Period6/04/216/04/21

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