ITM Web Conf.
Volume 46, 2022International Conference on Engineering and Applied Sciences (ICEAS’22)
|Number of page(s)||6|
|Section||Data Analysis and Image Processing|
|Published online||06 June 2022|
Detection of COVID-19 from chest radiology using histogram equalization combined with a CNN convolutional network
1 Mohammed V University in Rabat, higher school of technology, Laboratory of Systems Analysis, Information Processing and Industrial Management, Salé Morocco.
2 Mohammed V University in Rabat, higher school of technology, Laboratory of Systems Analysis, Information Processing and Industrial Management, Salé Morocco.
3 Mohammed V University in Rabat, higher school of technology, Laboratory of Systems Analysis, Information Processing and Industrial Management, Salé Morocco.
* Corresponding author: email@example.com
The world was shaken by the arrival of the corona virus (COVID-19), which ravaged all countries and caused a lot of human and economic damage. The world activity has been totally stopped in order to stop this pandemic, but unfortunately until today the world knows the arrival of new wave of contamination among the population despite the implementation of several vaccines that have been made available to the countries of the world and this is due to the appearance of new variants. All variants of this virus have recorded a common symptom which is an infection in the respiratory tract. In this paper a new method of detection of the presence of this virus in patients was implemented based on deep learning using a deep learning model by convolutional neural network architecture (CNN) using a COVID-QU chest X- ray imaging database. For this purpose, a pre-processing was performed on all the images used, aiming at unifying the dimensions of these images and applying a histogram equalization for an equitable distribution of the intensity on the whole of each image. After the pre-processing phase we proceeded to the formation of two groups, the first Train is used in the training phase of the model and the second called Test is used for the validation of the model. Finally, a lightweight CNN architecture was used to train a model. The model was evaluated using two metrics which are the confusion matrix which includes the following elements (ACCURACY, SPECIFITY, PRESITION, SENSITIVITY, F1_SCORE) and Receiver Operating Characteristic (the ROC curve). The results of our simulations showed an improvement after using the histogram equalization technique in terms of the following metrics: ACCURACY 96.5%, SPECIFITY 98.60% and PRESITION 98.66%.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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