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

TL;DR

This paper presents a rapid and accurate COVID-19 detection method from chest X-ray images by combining deep neural network features with LightGBM classification, achieving high accuracy on a public dataset.

Contribution

The study introduces a novel fusion of DenseNet169 and MobileNet features with LightGBM for COVID-19 classification, improving speed and accuracy over existing methods.

Findings

Achieved 98.54% accuracy in two-class classification.

Achieved 91.11% accuracy in multi-class classification.

Used Grad-CAM for interpretability analysis.

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.