Early detection of COVID19 by deep learning transfer Model for populations in isolated rural areas

Qjidaa, M.; Mechbal, Y.; Ben-fares, A.; Amakdouf, Hicham; Maâroufi, Mustapha; Alami, Badreeddine; Qjidaa, Hassan

doi:10.1109/iscv49265.2020.9204099

Cited by 19 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another obstacle faced by health organizations is the speed of obtaining the outcomes of the test, which usually ranges from hours to days [ 46 , 47 ]. The lack of accurate and fast disease detection approach can cause patients to spread the disease to the community without knowing that they are infected [ 48 ]. Furthermore, patients who require urgent medical care might not get suitable therapy at the right time.…”

Section: Discussionmentioning

confidence: 99%

Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh¹,

Nilashi²,

Ismail³

et al. 2022

Journal of Infection and Public Health

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Medical image processing and COVID-19: A literature review and bibliometric analysis

Abumalloh¹,

Nilashi²,

Ismail³

et al. 2022

Journal of Infection and Public Health

View full text Add to dashboard Cite

“…And the results indicated that the model achieved an accuracy of 92.00% in the multi-classification (COVID-19, pneumonia, and healthy chest X-ray images) and achieved an accuracy of 93.50% when distinguishing between COVID-19 chest X-ray images and non-COVID chest X-ray images. Qjidaa et al [119] adopted an ensemble classification method for COVID-19 diagnosis. They chose VGG16, VGG19, DenseNet121, MobileNet, Xception, InceptionV3, and InceptionResNetV2 to train, and each network produced a prediction.…”

Section: Covid-19 Diagnosis Based On Ensemble Learningmentioning

confidence: 99%

A Survey on Machine Learning in COVID-19 Diagnosis

Guo¹,

Zhang²,

Lu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What's more, the advantages and limitations of different diagnosis methods are compared. Although the great achievements in medical images classification in recent years, Corona Virus Disease 2019 images classification based on machine learning still encountered many problems. For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. Aiming at these problems, we propose some solutions and provide a comprehensive presentation for future research.

show abstract

“…Hal ini tentu menyebabkan delay waktu yang cukup lama untuk mengetahui hasil laboratorium. Belum lagi pengiriman yang memerlukan waktu yang cukup lama memungkinkan specimen uji terkontaminasi sehingga meningkatkan risiko terjadinya false negativity result [4]. Ketiga, hambatan pelaksanaan testing Covid-19 secara masif berasal dari sisi pasien suspect sendiri.…”

Section: Pendahuluanunclassified

Pemodelan Regresi Logistik untuk Diagnosis Dini Infeksi Covid-19 di Indonesia

Roosyidah

Supriyatna

2022

Jambura J. Math

View full text Add to dashboard Cite

Controlling the spread of the Covid-19 virus in Indonesia, the government continues to strive for a comprehensive 3T (Testing, Training, and Tracing) implementation. Massive testing is often constrained by several things, including cost and affordability of access. This study aims to create a model for early diagnosis of Covid-19 infection cases through several characteristic symptoms and experiences of close contact with positive patients. By using a binary logistic regression model, it was found that symptoms of anosmia, feverish symptoms, and close contact experience were significant in influencing Covid-19 infection. From the odds ratio value, it is known that anosmia is the most influential variable. Someone who has anosmia tends to be infected by 31 times compared to those who do not. Validation of the strength of the model in classifying is done by making predictions on the resulting model is good, because the measurement of each criterion of the strength of the model consists of accuracy, sensitivity, and specificity of the model both on the data testing each produces a value of 0.8 (close to 1). The area under the Receiver Operating Characteristic (ROC) curve for testing data is 0.8462, which means that the model already has good criteria for classifying.

show abstract

Early detection of COVID19 by deep learning transfer Model for populations in isolated rural areas

Cited by 19 publications

References 19 publications

Medical image processing and COVID-19: A literature review and bibliometric analysis

Medical image processing and COVID-19: A literature review and bibliometric analysis

A Survey on Machine Learning in COVID-19 Diagnosis

Pemodelan Regresi Logistik untuk Diagnosis Dini Infeksi Covid-19 di Indonesia

Contact Info

Product

Resources

About