Covid-19 classification using X-Ray imaging with ensemble learning

Siswantining, Titin; Parlindungan, Robinsar

doi:10.1088/1742-6596/1722/1/012072

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Artificial intelligence-based tools, particularly deep learning models, are the promising techniques used to assist radiologists in the early detection and diagnosis of coronavirus. Moreover, it reduces the workload of the radiologists, improves detection more accurately and efficiently, gives a timely response and accurate treatment for the patients of COVID-19 ( Balamurugan and Duraisamy, 2020 , Omoniyi et al, 2021 , Siswantining and Parlindungan, 2021 ). In this regard, the summary of various DL methods used in the detection and diagnosis of COVID-19 is presented in Table 6 , Table 7 .…”

Section: Applications Of Ai To Combat Covid-19mentioning

confidence: 99%

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Khan

Mehran

Haq

et al. 2021

Expert Systems with Applications

152

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During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

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Section: Applications Of Ai To Combat Covid-19mentioning

confidence: 99%

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Khan

Mehran

Haq

et al. 2021

Expert Systems with Applications

152

View full text Add to dashboard Cite

show abstract

“…In other similar studies, Foysal and Hossain [ 30 ] predicted the Covid-19 disease using CT images with 96% accuracy with the ensemble model, in which CNN classifiers were used together. Siswantining and Parlindungan [ 31 ] estimated the diagnosis of Covid-19 with 95% accuracy using X-ray images with the ensemble model developed with ANN, CNN and SVM classifiers. Li et al [ 32 ] aimed to predict the Covid-19 disease with a deep ensemble learning method consisting of VGG-16 algorithms.…”

Section: Related Workmentioning

confidence: 99%

Real-time internet of medical things framework for early detection of Covid-19

Yıldırım

Ci̇ci̇oğlu

Çalhan

2022

Neural Comput & Applic

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The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.

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“…The results showed that the best accuracy was obtained from the Stacking model with an accuracy of 95%. Researchers want this system to be able to display chest X-ray images [17]. Then research conducted by Windra Swastika proposed the detection of COVID-19 using deep learning based on the VGG16 algorithm with an accuracy of 92.86%.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm

Chaniago

Sugiharto

Khatulistiwa

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Corona Virus or also known as COVID-19 is one of the new viruses in 2019. Viruses caused by animal or human disease are called coronaviruses. Coronavirus will direct respiration in humans. Humans who are exposed to the corona virus will experience a respiratory infection. The research that will be made is useful for classifying X-rays of the lungs of patients affected by the coronavirus. In this study, the classification of coronaviruses focuses on three classes, namely Covid, Normal, and Viral Pneumonia. This study uses a lung X-ray image dataset. In this study there are 4 folders in it, namely Scenario 1, Scenario 2, Scenario 3, and Scenario 4. This study will use the Convolutional Neural Network (CNN) method by using an architectural model including Convolutional 2D, activation layers, max pooling layer, dropout layer , flatten, and finally dense layer. After building the model, in each scenario, the results of accuracy, precision, recall, and f1-score will be obtained. The result of accuracy of Scenario 1 is 97.87%, in Scenario 2 the accuracy is 94.84%, in Scenario 3 is 91.66%, and Scenario 4 is 91.41%.

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Covid-19 classification using X-Ray imaging with ensemble learning

Cited by 5 publications

References 2 publications

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Real-time internet of medical things framework for early detection of Covid-19

Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm

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