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

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%.