Deep Transfer Learning-based COVID-19 prediction using Chest X-rays

Kumar, Sheo; Mishra, Shweta; Singh, Sunil Kumar

doi:10.1101/2020.05.12.20099937

Cited by 21 publications

(19 citation statements)

References 30 publications

(27 reference statements)

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“…Kumar et al. [13] have proposed a model called DeQueezeNet which classify the patients X-ray images into two categories positive and negative while detecting the COVID-19. The proposed model predicts the possibility of the disease with 94.52% accuracy with the precision of 90.48%, by pre-processing the X-ray images of positive COVID-19 patients and normal cases.…”

Section: Applications Of Deep Learning For Detection Of Covid-19mentioning

confidence: 99%

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images

Panwar

Gupta

Siddiqui

et al. 2020

Chaos, Solitons & Fractals

410

227

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The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

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Section: Applications Of Deep Learning For Detection Of Covid-19mentioning

confidence: 99%

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images

Panwar

Gupta

Siddiqui

et al. 2020

Chaos, Solitons & Fractals

410

227

View full text Add to dashboard Cite

show abstract

“…To further leverage the discriminative power of different models, ensemble learning is deployed, where multiple deep nets are used to vote for the final results. For example, DeQueezeNet, proposed by Kumar et al (2020) (Zhou et al, 2016) were leveraged to detect and localize anomalous areas for COVID-19 diagnosis. Furthermore, Majeed et al (2020) have performed a comparison study on pretrained CNN models and deployed CAM to visualize the most discriminating regions.…”

Section: Methodologiesmentioning

confidence: 99%

“…To further leverage the discriminative power of different models, ensemble learning is deployed, where multiple deep nets are used to vote for the final results. For example, DeQueezeNet, proposed by Kumar et al (2020) , ensembles DenseNet and SqueezeNet for classification. Similar models were proposed by Goodwin et al (2020) ; Chatterjee et al (2020) ; Shibly et al (2020) ; Minaee et al (2020) ; Toǧaҫar et al (2020) Alternatively, Afshar et al (2020) haveintroduced a capsule network-based model for CXR diagnosis, where transfer learning is exploited to boost the performance.…”

Section: Covid-19 Detection and Diagnosismentioning

confidence: 99%

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Deng

2021

Front. Artif. Intell.

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Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients’ check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

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“…Different deep learning approaches including ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge are used in [153] to process CT and X-Ray images to identify the patients. In [154] , an ensemble of two types of transfer learning algorithms, namely DenseNet121 and SqueezeNet1.0 is proposed. In [155] AlexNet, GoogLeNet, Squeeznet, and Resnet18 are used as deep transfer learning models.…”

Section: Chest Computed Tomography and X-ray Image Processingmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

161

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Deep Transfer Learning-based COVID-19 prediction using Chest X-rays

Cited by 21 publications

References 30 publications

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images

A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Applications of artificial intelligence in battling against covid-19: A literature review

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