COVID-19 detection based on convolution neural networks from CT-scan images: a review

Ibrahim, Walat Ramadhan; Mahmood, Mayyadah Ramiz

doi:10.11591/ijeecs.v29.i3.pp1668-1677

Cited by 2 publications

(2 citation statements)

References 50 publications

(69 reference statements)

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“…The present pandemic state does not support the lowsensitivity RT-PCR test because RT-PCR takes considerable time and has a significant number of false-negative results (Ibrahim and Mahmood 2023). A test result that is falsely negative can result in an illness spreading extensively (Tabik et al 2020).…”

Section: Introduction 11 Overviewmentioning

confidence: 87%

Classified Covid-19 by Densenet121-Based Deep Transfer Learning From Ct-Scan Images

Ibrahim,

Mahmood

2023

SJUOZ

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The COVID-19 disease, which has recently emerged and has been considered a worldwide pandemic, has had a significant impact on the lives of millions of people and has forced a substantial load on healthcare organizations. Numerous deep-learning models have been utilized for diagnosing coronaviruses from chest computed tomography (CT) images. However, in light of the limited availability of datasets on COVID-19, the pre-trained deep learning networks were used. The main objective of this research is to construct and develop an automated approach for the early detection and diagnosis of COVID-19 in thoracic CT images. This paper proposes the DDTL-COV model, a deep transfer learning model based on DenseNet121, to classify patients on CT scans as either COVID or non-COVID, utilizing weights obtained from the ImageNet dataset. Two datasets were used to train the DDTL-COV model: the SARS-CoV-2 CT-scan dataset and the COVID19-CT dataset. In the SARS-CoV-2 CT dataset, the model achieved a good accuracy of 99.6%. However, on the second dataset (COVID19-CT dataset), its performance shows an accuracy rate of 89%. These results show that the model performed better than alternative methods.

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Section: Introduction 11 Overviewmentioning

confidence: 87%

Classified Covid-19 by Densenet121-Based Deep Transfer Learning From Ct-Scan Images

Ibrahim,

Mahmood

2023

SJUOZ

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“…To get the best hyperplane, SVM conducts a learning process. The separator function used is linear defined as in (12), and f(x) is formulated in (13).…”

Section: Classification Of Support Vector Machinementioning

confidence: 99%

Detection of chronic kidney disease using binary whale optimization algorithm

Sutikno,

Kusumaningrum,

Sugiharto

et al. 2024

IJ-AI

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Chronic kidney disease (CKD), a medical illness, is characterized by a steady deterioration in kidney function. A disease's ability to be prevented and effectively significantly treated depends on early diagnosis. The addition of filter feature selection to the machine learning algorithm has been done to detect CKD. However, the quality of its feature subset is not optimal. Wrapper feature selection can improve the quality of these feature subsets. Therefore, we proposed wrapper feature selection and binary whale optimization algorithm (BWOA) to enhance the accuracy of early CKD detection. We also make data improvements to improve accuracy, namely the preprocessing process with the median and modus techniques. We used a public dataset of 250 medical records of kidney sufferers and 150 completely healthy people. There are 24 features in this dataset. The test results showed that adding BWOA feature selection can increase accuracy. The proposed method produced an accuracy of 100%. Further research on these methods can be used to develop expert systems for early detection of CKD.

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COVID-19 detection based on convolution neural networks from CT-scan images: a review

Cited by 2 publications

References 50 publications

Classified Covid-19 by Densenet121-Based Deep Transfer Learning From Ct-Scan Images

Classified Covid-19 by Densenet121-Based Deep Transfer Learning From Ct-Scan Images

Detection of chronic kidney disease using binary whale optimization algorithm

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