COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images

Abstract

The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.

Prognostic Nutritional Index, Controlling Nutritional Status (CONUT) Score, and Inflammatory Biomarkers as Predictors of Deep Vein Thrombosis, Acute Pulmonary Embolism, and Mortality in COVID-19 Patients

“…Numerous recent studies have also shown the superior role of superficial and deep learning-based intelligence in the diagnosis of COVID-19 patients using X-rays and CT scans with higher accuracy than the standard diagnostic approach [ 65 , 66 , 67 , 68 , 69 , 70 ].…”

Health Informatics: The Foundations of Public Health

“…The pandemic has also generated extensive datasets related to COVID-19. The vast amount of COVID-19 data presents numerous health informatics research opportunities [ 4 , 5 , 6 ].…”

“…To combat the spread of the virus and reduce the risk of infection, implementing an autonomous detection system is crucial. Therefore, in the study of Ayadi et al [ 4 ], an automated system for COVID-19 detection and diagnosis was constructed. The COVID-19 detection function of their system was built with a neural network that concatenated AlexNet and Xception models.…”

A Robust Hybrid Deep Convolutional Neural Network for COVID-19 Disease Identification from Chest X-ray Images

“…COVID-AleXception is proposed in [17], which is a concatenation of the features from two pre-trained CNN methods, Xception and AlexNet. The dataset comprises 15,153 X-ray images (1345 pneumonia, 3616 COVID-19 and 10,192 normal).…”