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Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

Hamid Nasiri, Ghazal Kheyroddin, Morteza Dorrigiv, Mona Esmaeili, Amir Raeisi Nafchi, Mohsen Haji Ghorbani, Payman Zarkesh-Ha

TL;DR

This article proposes a new technique that is faster and more accurate than the other methods reported in the literature and uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images.

Abstract

The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for further analysis.

Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

TL;DR

This article proposes a new technique that is faster and more accurate than the other methods reported in the literature and uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images.

Abstract

The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for further analysis.
Paper Structure (12 sections, 2 equations, 5 figures, 4 tables)

This paper contains 12 sections, 2 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Prerequisites
  4. Deep Neural Networks
  5. Univariate Feature Selection
  6. LightGBM
  7. Gradient-based One-Side Sampling (GOSS)
  8. Exclusive Feature Bundling (EFB)
  9. Proposed Method
  10. Experimental Results
  11. Conclusion
  12. Code Availability

Figures (5)

  • Figure 1: Framework of the proposed method. Pre-trained DenseNet169 and MobileNet extract features separately. Then these features are combined together to increase classification accuracy. Best features are selected to improve the computational complexity of the algorithm. At the end LightGBM is fed with selected features and classifies the new chest X-ray images.
  • Figure 2: Sample Images in the Dataset
  • Figure 3: The Confusion Matrix (Three-Class Problem)
  • Figure 4: The Confusion Matrix (Two-Class Problem)
  • Figure 5: The output of GradCAM for three sample images (a) Original image (b) Class activation heatmap (c) Superimposed image.