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

Nasiri, Hamid and Kheyroddin, Ghazal and Dorrigiv, Morteza and Esmaeili, Mona and Nafchi, Amir Raeisi and Ghorbani, Mohsen Haji and Zarkesh-Ha, Payman (2022) Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM. In: 2022 IEEE World AI IoT Congress, AIIoT 2022 :. 2022 IEEE World AI IoT Congress, AIIoT 2022 . Institute of Electrical and Electronics Engineers Inc., USA, pp. 201-206. ISBN 9781665484534

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 future analysis.