A Deep-Learning-Based Framework for Automated Diagnosis of COVID-19 Using X-ray Images

Abstract

The emergence and outbreak of the novel coronavirus (COVID-19) had a devasting effect on global health, the economy, and individuals’ daily lives. Timely diagnosis of COVID-19 is a crucial task, as it reduces the risk of pandemic spread, and early treatment will save patients’ life. Due to the time-consuming, complex nature, and high false-negative rate of the gold-standard RT-PCR test used for the diagnosis of COVID-19, the need for an additional diagnosis method has increased. Studies have proved the significance of X-ray images for the diagnosis of COVID-19. The dissemination of deep-learning techniques on X-ray images can automate the diagnosis process and serve as an assistive tool for radiologists. In this study, we used four deep-learning models—DenseNet121, ResNet50, VGG16, and VGG19—using the transfer-learning concept for the diagnosis of X-ray images as COVID-19 or normal. In the proposed study, VGG16 and VGG19 outperformed the other two deep-learning models. The study achieved an overall classification accuracy of 99.3%.

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

“…Using the relevant datasets of chest X-ray images for the COVID-19 detection is a laborious task. The researchers used different preprocessing techniques, feature extraction techniques and classification methods [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Now, it is difficult to suggest a promising technique or combination of techniques that are more effective in diagnosing COVID-19 from the chest X-ray image.…”

“…Khan et al [ 34 ] developed a new architecture for the diagnosis of X-ray images as the COVID-19 or normal using pre-trained deep learning models like ResNet50, VGG16, VGG19 and DensNet121, where VGG16 and VGG19 showed the best accuracies. The proposed model consisted of two phases like preprocessing and data augmentation, and transfer learning, and finally showed 99.3% accuracy.…”

Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database

“…In this section, a comprehensive comparison of the proposed method is made with existing state-of-the-art deep feature-based CAD methods related to COVID19 diagnostics [13] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] . Most of these methods used the existing pretrained networks and applied the end-to-end transfer learning approach for the automated diagnosis of COVID19 infection.…”

“…However, these comparative studies used limited radiographic datasets and different experimental protocols. For a fair comparison, the quantitative results of these baseline methods [13] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] were assessed based on the selected datasets and experimental protocol. In details, the pretrained backbones of the baseline methods [13] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] were selected and fine-tuned with the selected datasets.…”

“…Recent advancements in artificial intelligence (AI) have shown a remarkable breakthrough in the development of computer-aided diagnosis (CAD) tools [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . In particular, with the advent of deep learning algorithms, such automated tools have shown significant decision-making performance in different medical domains, including radiology.…”