Transfer-to-Transfer Learning Approach for Computer Aided Detection of COVID-19 in Chest Radiographs

Narayanan, Barath Narayanan; Hardie, Russell C.; Krishnaraja, Vignesh; Karam, Christina; Davuluru, Venkata Salini Priyamvada

doi:10.3390/ai1040032

Cited by 31 publications

(18 citation statements)

References 45 publications

(72 reference statements)

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“…As of 18 March 2021—a year after the WHO categorized the COVID-19 outbreak as a pandemic, there were 120,915,219 cumulative cases and 2,674,078 total deaths [ 1 ]. The number of infected cases is believed to be underreported because of asymptomatic infection, hesitancy to get tested, and lack of alternative testing procedures [ 2 , 3 , 4 , 5 ]. The pandemic disrupted many aspects of human activity, leading to worldwide economic challenges including increased unemployment, income, food, and housing insecurity, and intimate partner violence [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Willing or Hesitant? A Socioeconomic Study on the Potential Acceptance of COVID-19 Vaccine in Japan

Kadoya

Watanapongvanich

Yuktadatta

et al. 2021

IJERPH

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The worldwide COVID-19 vaccination program is already underway, raising hopes and aspirations to contain the spread of the COVID-19 pandemic that halted economic and social activities. However, the issue of vaccine effectiveness and its side-effects is influencing the potential acceptance of vaccines. In this uncertain situation, we used data from a nationwide survey in Japan during February 2021, following the Japanese government’s initial phase of COVID-19 vaccination. Our results show that 47% of the respondents are willing to take a vaccine once it is available, while 22% are not willing and another 31% remain indecisive. Our ordered probit regression results show that demographic, socioeconomic, and behavioral variables such as gender, age, subjective health status, children, household income, household assets, financial literacy, future anxiety, and myopic view of the future are associated with willingness to take a COVID-19 vaccine. Our findings suggest that Japan’s government should not adopt a one-size-fits-all policy to promote the vaccination program, but rather target people with specific socioeconomic backgrounds who are less willing and more hesitant to take a vaccine.

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Section: Introductionmentioning

confidence: 99%

Willing or Hesitant? A Socioeconomic Study on the Potential Acceptance of COVID-19 Vaccine in Japan

Kadoya

Watanapongvanich

Yuktadatta

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…A hybrid model combining CNN and Bi-LSTM was designed for COVID infection detection from CT scans [ 5 ]. Transfer learning based CAD system is built using chest radio graph images [ 36 ]. In contrast to traditional transfer learning, a deep transfer learning based approach was proposed to identify corona infection from chest X-ray images [ 31 ].…”

Section: Background Studymentioning

confidence: 99%

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Shaik

Cherukuri

2022

Computers in Biology and Medicine

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Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.

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“…Currently, ML/DL has played a significant role in boosting many CAD systems' diagnostic efficiency for various medical applications such as diagnosing and detecting different pulmonary diseases. Recent studies have shown that DL techniques have proven to be very efficient for CAD systems in radiography [48]. Hence, C.T.…”

Section: Investigation On Classification Performancementioning

confidence: 99%

COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review

et al. 2021

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COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies in the medical domain also advocates its significant role in COVID-19 detection. This systematic review presents ML and DL techniques practiced in this era to predict, diagnose, classify, and detect the coronavirus. In this study, the data was retrieved from three prevalent full-text archives, i.e., Science Direct, Web of Science, and PubMed, using the search code strategy on 16 March 2021. Using professional assessment, among 961 articles retrieved by an initial query, only 40 articles focusing on ML/DL-based COVID-19 detection schemes were selected. Findings have been presented as a country-wise distribution of publications, article frequency, various data collection, analyzed datasets, sample sizes, and applied ML/DL techniques. Precisely, this study reveals that ML/DL technique accuracy lay between 80% to 100% when detecting COVID-19. The RT-PCR-based model with Support Vector Machine (SVM) exhibited the lowest accuracy (80%), whereas the X-ray-based model achieved the highest accuracy (99.7%) using a deep convolutional neural network. However, current studies have shown that an anal swab test is super accurate to detect the virus. Moreover, this review addresses the limitations of COVID-19 detection along with the detailed discussion of the prevailing challenges and future research directions, which eventually highlight outstanding issues.

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Transfer-to-Transfer Learning Approach for Computer Aided Detection of COVID-19 in Chest Radiographs

Cited by 31 publications

References 45 publications

Willing or Hesitant? A Socioeconomic Study on the Potential Acceptance of COVID-19 Vaccine in Japan

Willing or Hesitant? A Socioeconomic Study on the Potential Acceptance of COVID-19 Vaccine in Japan

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review

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