Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Oh, Yung-Hwan; Park, Sang Joon; Ye, Jong Chul

doi:10.1109/tmi.2020.2993291

Cited by 795 publications

(720 citation statements)

References 25 publications

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“…Furthermore, automated segmentation of COVID-19 infections in chest X-ray scans is the main prospect of this research work. This segmentation task will significantly assist the clinician to follow-up the disease progress in the lung of infected patients, as described in [ 57 ]. To satisfy security and privacy requirements for transmitting medical images over general communication networks [ 58 ], securing COVID-19 patient data will be also considered in the next version of our developed deep learning framework.…”

Section: Discussionmentioning

confidence: 99%

Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

Karar

Hemdan

Shouman

2020

Complex Intell. Syst.

126

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Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes a new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed deep learning framework constitutes two major advancements as follows. First, complicated multi-label classification of X-ray images have been simplified using a series of binary classifiers for each tested case of the health status. That mimics the clinical situation to diagnose potential diseases for a patient. Second, the cascaded architecture of COVID-19 and pneumonia classifiers is flexible to use different fine-tuned deep learning models simultaneously, achieving the best performance of confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such as Visual Geometry Group Network (VGG) and Residual Neural Network (ResNet). They have been successfully tested and evaluated on public X-ray image dataset for normal and three diseased cases. The results of proposed cascaded classifiers showed that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of COVID-19, viral (Non-COVID-19) pneumonia, and bacterial pneumonia images, respectively. Furthermore, the performance of our cascaded deep learning classifiers is superior to other multi-label classification methods of COVID-19 and pneumonia diseases in previous studies. Therefore, the proposed deep learning framework presents a good option to be applied in the clinical routine to assist the diagnostic procedures of COVID-19 infection.

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Section: Discussionmentioning

confidence: 99%

Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

Karar

Hemdan

Shouman

2020

Complex Intell. Syst.

126

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“…[75] have proposed a Deep Convolutional Neural Network design named COVID-Net using a lightweight residual projection-expansion projection-extension design pattern. The work by [54] proposes a patch-based convolutional neural network approach with a relatively small number of trainable parameters along with statistical analysis of the potential imaging biomarkers of the chest X-Rays.…”

Section: Related Workmentioning

confidence: 99%

COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays

Singh

Pandey

Babu

2020

Preprint

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COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delay in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel Deep Learning based solution to rapidly classify COVID -19 patient using chest X-Ray. The proposed solution uses image enhancement, image segmentation and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify Chest X-Ray into three classes viz. COVID-19, Pneumonia and Normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalisability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualisation in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state of the art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-Rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67\% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, Normal, and Pneumonia classes respectively on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold standard clinical and laboratory testing.

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“…Covid-chestxray-dat aset https://github.com/iee e8023/covid-chestxra y-dataset [1], [3], [7], [10], [15], [17], [18], [20], [22], [23], [ 25], [31], [32], [40], [48], [49], [51], [54], [61], [66], [67], [70], [71], [72], [73] 25…”

Section: Number Of Papersmentioning

confidence: 99%

A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

Panday¹

2020

Preprint

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As COVID-19 is the source of millions of deaths throughout the world, it turned obligatory to fight against the COVID-19 pandemic. Due to the need for expensive equipment, experienced radiologists, and the time-consuming in Reverse Transcription Polymerase Chain Reaction (RT-PCR) test, researchers find out the necessity to embrace X-ray images and Computed Tomography (CT) images based diagnosing. Wreak havoc of COVID-19 instigated me to review current emerging Artificial Intelligence(AI) based automatic diagnosing models through the statistical survey that will pave out the way of research. In this paper, I study different available research resources at the time span from April 2020 to July 2020. In order to help researchers in further research, I presented a statistical survey so that researchers can pick a preeminent diagnosing model. I took a look at 74 papers from April to July and specified preprocessing techniques, feature extraction, classification method, interpretability method, and experimental result. Moreover, I analyze training,testing and validation split ratio, as well as look into the dataset’s availability publicly. Some researchers are able to gain noticeable performance by adopting their own local model. On the contrary, some researchers adopt an existing pre-trained model and achieve the utmost result. Some models need to feed huge data and some models outperform despite having small data. In the following sections, all of the criteria will be illustrated briefly.

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Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Cited by 795 publications

References 25 publications

Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays

A Complete Survey on Automatically Diagnosing COVID-19 In the field of Computer Vision and A Collection of Medical Images

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