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Published on: December 19, 2020
Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach
Mazhar Javed Awan1, Muhammad Haseeb Bilal1, Awais Yasin2
1Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan.
Insights
This study used deep transfer learning with Convolutional Neural Networks (CNNs) on chest X-rays to detect COVID-19. The models achieved high accuracy, aiding in rapid diagnosis of the novel coronavirus.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Coronavirus disease (COVID-19) poses a global health challenge due to rapid transmission and difficulties in early detection and treatment.
- Overcrowding of hospitals by patients with COVID-19 symptoms presents a significant healthcare system challenge.
- Deep learning offers promising advancements in medical research and diagnostic techniques for complex diseases.
Purpose of the Study:
- To develop and evaluate a deep transfer learning (DTL) classifier for detecting COVID-19 from chest X-ray images.
- To assess the performance of three distinct Convolutional Neural Network (CNN) architectures (InceptionV3, ResNet50, VGG19) within the Apache Spark framework.
- To differentiate between COVID-19 positive, normal, and pneumonia X-ray images.
Main Methods:
- Utilized the Apache Spark system as a big data framework for processing medical images.
- Applied Deep Transfer Learning (DTL) with three CNN architectures: InceptionV3, ResNet50, and VGG19.
- Trained and evaluated the models on chest X-ray images for binary (COVID-19 vs. Normal) and ternary (COVID-19 vs. Normal vs. Pneumonia) classification tasks.
Main Results:
- Achieved 100% accuracy in classifying COVID-19 positive versus normal X-ray images across all three models.
- Demonstrated high detection accuracy in the ternary classification task: 97% for InceptionV3, 98.55% for ResNet50, and 98.55% for VGG19.
- The ResNet50 and VGG19 models showed superior performance in distinguishing between COVID-19, normal, and pneumonia cases.
Conclusions:
- Deep transfer learning models, particularly ResNet50 and VGG19, show significant potential for accurate COVID-19 detection using chest X-rays.
- The integration of Apache Spark facilitates the application of complex deep learning models on large medical datasets.
- These findings contribute to advancing automated diagnostic tools for infectious respiratory diseases like COVID-19.
Abstract:
Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning's contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures -InceptionV3, ResNet50, and VGG19-on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.
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