Comparison of transfer learning method for COVID-19 detection using convolution neural network

Imaduddin, Helmi; Yusfida, Fiddin; Fatmawati, Azizah; Hermansyah, Brian Aditya

doi:10.11591/eei.v11i2.3525

Cited by 7 publications

(17 citation statements)

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“…In previous research, Musleh and Maghari [28] using the CheXNet approch obtained accuracy of 89.7%, compared to this study with accuracy 99.28%. Wang et al [29] using using the Covid-Net approach to detect covid in x-ray images obtained a sensitivity of 80%, compared to this study with 99.61% sensitivity, in addition Imaduddin et al [11] using ResNet 50 to get 92.3% accuracy, 93% precision, 93% F1-score, 99% sensitivity, and 90.7% specificity compared to the research conducted with 99.28% accuracy, 99.41% precision, 99.51% F1-Score, 99.61% sensitivity, and 98.33% specificity.…”

Section: Resultssupporting

confidence: 45%

“…In another study Ismael [10], using 380 X-ray images data achieved F1 score of 95.92% on ResNet50 features and SVM approch. Another study Imaduddin [11] using ResNet 50 obtained 92.3% accuracy, 93% F1-score, 93% precision, 90.7% specificity and 99% sensitivity on X-ray datasets. Research conducted now will be compared with research that has been done before.…”

Section: Introductionmentioning

confidence: 99%

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Transfer learning for detecting COVID-19 on x-ray using deep residual network

Imaduddin

Hermansyah

2022

Bulletin EEI

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Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.

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Section: Resultssupporting

confidence: 45%

Section: Introductionmentioning

confidence: 99%

Transfer learning for detecting COVID-19 on x-ray using deep residual network

Imaduddin

Hermansyah

2022

Bulletin EEI

Self Cite

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“…The classifier part comprises a fully connected layer. Arrangements of CNN shall construct various forms of CNN architectures such as AlexNet [8], VGG [9], and ResNet [10].…”

Section: Methods 21 State-of-the-art Techniquesmentioning

confidence: 99%

“…The transfer learning approach has been chosen for the approach of this research because the technique has utilized best practices for state-of-the-art models [5]- [7]. Particularly, the trained models for detecting building shapes from given images employ convolutional neural networks (CNN) architectures such as AlexNet [8], visual geometry group (VGG) [9], and ResNet [10]. Furthermore, we postulate that by using a low complexity pre-processing algorithm, the entire transfer learning process will be more efficient.…”

Section: Introductionmentioning

confidence: 99%

Masking preprocessing in transfer learning for damage building detection

Toba

Bunyamin

Widyaya

et al. 2023

IJ-AI

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The sudden climate change occurring in different places in the world has made disasters more unpredictable than before. In addition, responses are often late due to manual processes that have to be performed by experts. Consequently, major advances in computer vision (CV) have prompted researchers to develop smart models to help these experts. We need a strong image representation model, but at the same time, we also need to prepare for a deep learning environment at a low cost. This research attempts to develop transfer learning models using low-cost masking pre-processing in the experimental building damage (xBD) dataset, a large-scale dataset for advancing building damage assessment. The dataset includes eight types of disasters located in fifteen different countries and spans thousands of square kilometers of satellite images. The models are based on U-Net, i.e., AlexNet, visual geometry group (VGG)-16, and ResNet-34. Our experiments show that ResNet-34 is the best with an F1 score of 71.93%, and an intersection over union (IoU) of 66.72%. The models are built on a resolution of 1,024 pixels and use only first-tier images compared to the state-of-the-art baseline. For future orientations, we believe that the approach we propose could be beneficial to improve the efficiency of deep learning training.

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“…There is a wide range of applications in artificial intelligence, machine learning, automated machine learning and deep learning approaches that have helped greatly throughout the worrying journey of COVID-19 [10]. Starting from detecting the disease [11], diagnosis [12], repurposing treatments, vaccine development, pandemic management [13], and up to the post vaccination statistics and findings, are all the applications of AI and machine learning for the goal of forcing the pandemic to its end.…”

Section: Automated Learning Role In Phases Of Covid-19mentioning

confidence: 99%

Evolution of automated learning techniques for combating COVID-19: an analysis

Mohammed

Hamsheen

2022

IJEECS

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It is now more than two years that the world is battling the tiny invisible virus, COVID-19. Since its appearance, it showered humankind with shock, fear, and death. In small words, this pandemic has paused human life in all its aspects and beauties. Governments, health industry researchers and laboratories have put all their efforts to achieve a universal goal that is, overcoming the crisis and putting an end to the pandemic. However, this goal was never achievable without the smart use of automated learning, artificial intelligence, machine learning and deep learning algorithms. This review paper presents a collection of the experimental research articles tackled using real-time official datasets from hospitals and governments. These datasets are processed using automated learning (AL) algorithms in order to find suitable solutions to most of the COVID-19 related problems. This paper presents the AL applications in a story telling manner, starting from the first phases of COVID-19, when doctors had no experience dealing with the disease and had difficulty in diagnosing it, then moving to the other phases like suggesting a medicine, drug repurposing, facial mask detection, fake news detection, vaccine development, pandemic management, post vaccine statistics and lastly post COVID-19 analysis.

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Comparison of transfer learning method for COVID-19 detection using convolution neural network

Cited by 7 publications

References 0 publications

Transfer learning for detecting COVID-19 on x-ray using deep residual network

Transfer learning for detecting COVID-19 on x-ray using deep residual network

Masking preprocessing in transfer learning for damage building detection

Evolution of automated learning techniques for combating COVID-19: an analysis

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