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

• URL: http://arxiv.org/abs/2206.04548v1
• Date: Thu, 9 Jun 2022 14:56:24 GMT
• Title: Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM
• Authors: Hamid Nasiri, Ghazal Kheyroddin, Morteza Dorrigiv, Mona Esmaeili, Amir Raeisi Nafchi, Mohsen Haji Ghorbani, Payman Zarkesh-Ha
• Abstract summary: 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. The method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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