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The use of CLAHE for improving an accuracy of CNN architecture for detecting pneumonia

Authors :
Tjoa Elbert Alfredo
Yowan Nugraha Suparta I Putu
Magdalena Rita
Kumalasari CP Nor
Source :
SHS Web of Conferences, Vol 139, p 03026 (2022)
Publication Year :
2022
Publisher :
EDP Sciences, 2022.

Abstract

Artificial intelligence (AI) has now grown rapidly for helping many aspects of human life, one of them is for medical image processing. Currently, the world is still suffering from COVID-19 pandemic outbreak which affects more than 36 million people and it is estimated that more than 1 million death occurred as a result of this outbreak. Early detection for COVID-19 suffers is needed to assist doctors and medical experts to determine the next medication for patients for avoiding the worsening condition which leads to death. AI-based model is can be used for assisting medical experts for detecting and classify the lung condition based on chest x-ray (CXR) patient‗s image accurately by using deep learning. On this paper, authors proposed the use on contrast limited adaptive histogram equalization (CLAHE) for pre-processing the medical images combined with CNN AlexNet architecture. The result of this method then compared with non-CLAHE CNN AlexNet also self-made CNN architecture. The result shows a promising result by the accuracy of CNN AlexNet architecture is 91.11%.

Subjects

Subjects :
Social Sciences

Details

Language :
English, French
ISSN :
22612424
Volume :
139
Database :
Directory of Open Access Journals
Journal :
SHS Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.2968bf23106849f9936f3062737b3496
Document Type :
article
Full Text :
https://doi.org/10.1051/shsconf/202213903026