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The use of CLAHE for improving an accuracy of CNN architecture for detecting pneumonia
- 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 :
- Social Sciences
Subjects
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