Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique

Despite the vaccinations; the emergence of new and more contagious variants of the COVID-19 disease has continued to pose threats and challenges to our lives. Until herd immunity is achieved; it is important to continuously perform screening tests to control and minimize the transmissions. Due to th...

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Main Authors: Bacad, Dave Jammin A, Abu, Patricia Angela R
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Published: Archīum Ateneo 2021
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/246
https://ieeexplore.ieee.org/document/9664676
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.discs-faculty-pubs-12592022-02-23T08:48:17Z Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique Bacad, Dave Jammin A Abu, Patricia Angela R Despite the vaccinations; the emergence of new and more contagious variants of the COVID-19 disease has continued to pose threats and challenges to our lives. Until herd immunity is achieved; it is important to continuously perform screening tests to control and minimize the transmissions. Due to the reported shortcomings of the RT-PCR; the utilization of deep learning for detecting COVID-19 from Chest X-Ray (CXR) images has gathered a lot of interest from researchers. As a contribution to the field; this study proposes a deep learning pipeline that utilizes transfer learning and image enhancement techniques to classify whether a given CXR image exhibits characteristics of COVID-19 infection; pneumonia infection; or normal/healthy lungs. For a lighter approach; the small pre-trained model named EfficientNetB0 is used as the base model for the transfer learning method. To improve the network’s performance; a sequence of contrast enhancement techniques namely the Multi-Scale Retinex (MSR) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is introduced in the pipeline and employed as a pre-processing step. Gathered from a 10-fold cross-validation method in a dataset with 3729 images per class; results show that the proposed approach achieves an average overall accuracy of 92.089% with 98.431% average precision; 95.119% average recall; and 96.741% average f1-score for the COVID-19 class. 2021-01-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/246 https://ieeexplore.ieee.org/document/9664676 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo COVID-19 deep learning adaptation models pulmonary diseases transfer learning pipelines lung chest x-ray image processing Computer Sciences Databases and Information Systems Public Health Pulmonology
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic COVID-19
deep learning
adaptation models
pulmonary diseases
transfer learning
pipelines
lung
chest x-ray
image processing
Computer Sciences
Databases and Information Systems
Public Health
Pulmonology
spellingShingle COVID-19
deep learning
adaptation models
pulmonary diseases
transfer learning
pipelines
lung
chest x-ray
image processing
Computer Sciences
Databases and Information Systems
Public Health
Pulmonology
Bacad, Dave Jammin A
Abu, Patricia Angela R
Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique
description Despite the vaccinations; the emergence of new and more contagious variants of the COVID-19 disease has continued to pose threats and challenges to our lives. Until herd immunity is achieved; it is important to continuously perform screening tests to control and minimize the transmissions. Due to the reported shortcomings of the RT-PCR; the utilization of deep learning for detecting COVID-19 from Chest X-Ray (CXR) images has gathered a lot of interest from researchers. As a contribution to the field; this study proposes a deep learning pipeline that utilizes transfer learning and image enhancement techniques to classify whether a given CXR image exhibits characteristics of COVID-19 infection; pneumonia infection; or normal/healthy lungs. For a lighter approach; the small pre-trained model named EfficientNetB0 is used as the base model for the transfer learning method. To improve the network’s performance; a sequence of contrast enhancement techniques namely the Multi-Scale Retinex (MSR) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is introduced in the pipeline and employed as a pre-processing step. Gathered from a 10-fold cross-validation method in a dataset with 3729 images per class; results show that the proposed approach achieves an average overall accuracy of 92.089% with 98.431% average precision; 95.119% average recall; and 96.741% average f1-score for the COVID-19 class.
format text
author Bacad, Dave Jammin A
Abu, Patricia Angela R
author_facet Bacad, Dave Jammin A
Abu, Patricia Angela R
author_sort Bacad, Dave Jammin A
title Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique
title_short Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique
title_full Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique
title_fullStr Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique
title_full_unstemmed Detecting COVID-19 from Chest X-Ray Images using a Lightweight Deep Transfer Learning Model with Improved Contrast Enhancement Technique
title_sort detecting covid-19 from chest x-ray images using a lightweight deep transfer learning model with improved contrast enhancement technique
publisher Archīum Ateneo
publishDate 2021
url https://archium.ateneo.edu/discs-faculty-pubs/246
https://ieeexplore.ieee.org/document/9664676
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