Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak, Soumya Ranjan; Nayak, Deepak Ranjan; Sinha, Utkarsh; Arora, Vaibhav; Pachori, Ram Bilas

doi:10.1016/j.bspc.2020.102365

Cited by 317 publications

(206 citation statements)

References 32 publications

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“…The deep learning model developed in works of ( Apostolopoulos and Mpesiana, 2020 ), trained on 1,428 CXR images, can achieve diagnostic accuracy of 94% on an imbalanced testing set. Similar performance (96.8%) was achieved by works of ( Nayak et al, 2021 ) using a pre-trained network on the ChestX-ray8 dataset, and works of ( Brunese et al, 2020 ) with accuracy of 98%. The patch-based network developed by ( Oh et al, 2020 ) can perform 5-classes diagnosis (normal, bacterial, tuberculosis, viral and COVID-19) of the patients with averaged accuracy of 88.9% and provides interpretable saliency maps.…”

Section: Introductionsupporting

confidence: 65%

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

Zhong

et al. 2021

Medical Image Analysis

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In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.

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Section: Introductionsupporting

confidence: 65%

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

Zhong

et al. 2021

Medical Image Analysis

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“…Their study showed that the ResNet-50 model with SVM provides the best result. Similar work was done by Nayak et al [ 12 ] in which they compared the performance of eight pre-trained CNN models for the diagnosis of COVID-19. The best performance is obtained by the ResNet-34 model with an accuracy (ACC) of 98.33%.…”

Section: Introductionmentioning

confidence: 83%

FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

Chaudhary

Pachori

2021

Computers in Biology and Medicine

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“…However, these models use a single deep learning network whose processing efficacy and accuracy remain to be further improved. According to Soumya Ranjan Nayak et al‘s comprehensive study [ 17 ], the further development of effective deep CNN models for a more accurate diagnosis of COVID-19 infection is still in urgent need because the maximum accuracy value of single CNNs did not exceed 98.33% for binary classification (COVID-19 versus normal).…”

Section: Introductionmentioning

confidence: 99%

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph

Jin

Dong

et al. 2021

Computers in Biology and Medicine

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Background Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients’ lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement. Methods In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features. Results Compared to five existing models (InceptionV3: 97.916 ± 0.408%; SqueezeNet: 97.189 ± 0.526%; VGG19: 96.520 ± 1.220%; ResNet50: 97.476 ± 0.513%; ResNet101: 98.241 ± 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 ± 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 ± 0.001s; InceptionV3: 12.376 ± 0.002s; ResNet50: 10.952 ± 0.001s; ResNet101: 18.040 ± 0.002s; VGG19: 16.632 ± 0.002s; proposed model: 5.917 ± 0.001s). Conclusion The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes.

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Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Cited by 317 publications

References 32 publications

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

FBSED based automatic diagnosis of COVID-19 using X-ray and CT images

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph

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