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10.1016/j.irbm.2021.01.004 http://scihub22266oqcxt.onion/10.1016/j.irbm.2021.01.004 33527035!7839628!33527035     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33527035&cmd=llinks): Failed to open stream: HTTP request failed! 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COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network |pdf=|usr=}}{{33527035}} gab.com Text COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network JO: Ing Rech Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33527035 #FOAvip OBJECTIVES: Coronavirus disease is a fatal epidemic that has originated in Wuhan, China in December 2019. This disease is diagnosed using radiological images taken with the help of basic scanning methods besides the test kits for Reverse Transcription Polymerase Chain Reaction (RT-PCR). Automatic analysis of chest Computed Tomography (CT) images that are based on image processing technology plays an important role in combating this infectious disease. MATERIAL AND METHODS: In this paper, a new Multiple Kernels-ELM-based Deep Neural Network (MKs-ELM-DNN) method is proposed for the detection of novel coronavirus disease - also known as COVID-19, through chest CT scanning images. In the model proposed, deep features are extracted from CT scan images using a Convolutional Neural Network (CNN). For this purpose, pre-trained CNN-based DenseNet201 architecture, which is based on the transfer learning approach is used. Extreme Learning Machine (ELM) classifier based on different activation methods is used to calculate the architecture's performance. Lastly, the final class label is determined using the majority voting method for prediction of the results obtained from each architecture based on ReLU-ELM, PReLU-ELM, and TanhReLU-ELM. RESULTS: In experimental works, a public dataset containing COVID-19 and Non-COVID-19 classes was used to verify the validity of the MKs-ELM-DNN model proposed. According to the results obtained, the accuracy score was obtained as 98.36% using the MKs-ELM-DNN model. The results have demonstrated that, when compared, the MKs-ELM-DNN model proposed is proven to be more successful than the state-of-the-art algorithms and previous studies. CONCLUSION: This study shows that the proposed Multiple Kernels-ELM-based Deep Neural Network model can effectively contribute to the identification of COVID-19 disease. Twit Text FOAVip COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network ????Ing Rech Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33527035 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33527035 #FOAvip English Wikipedia <ref>{{Cite pmid|33527035}}</ref> COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network #MMPMID33527035 Turkoglu M Ing Rech Biomed 2021[Aug]; 42 (4): 207-214 PMID33527035show ga OBJECTIVES: Coronavirus disease is a fatal epidemic that has originated in Wuhan, China in December 2019. This disease is diagnosed using radiological images taken with the help of basic scanning methods besides the test kits for Reverse Transcription Polymerase Chain Reaction (RT-PCR). Automatic analysis of chest Computed Tomography (CT) images that are based on image processing technology plays an important role in combating this infectious disease. MATERIAL AND METHODS: In this paper, a new Multiple Kernels-ELM-based Deep Neural Network (MKs-ELM-DNN) method is proposed for the detection of novel coronavirus disease - also known as COVID-19, through chest CT scanning images. In the model proposed, deep features are extracted from CT scan images using a Convolutional Neural Network (CNN). For this purpose, pre-trained CNN-based DenseNet201 architecture, which is based on the transfer learning approach is used. Extreme Learning Machine (ELM) classifier based on different activation methods is used to calculate the architecture's performance. Lastly, the final class label is determined using the majority voting method for prediction of the results obtained from each architecture based on ReLU-ELM, PReLU-ELM, and TanhReLU-ELM. RESULTS: In experimental works, a public dataset containing COVID-19 and Non-COVID-19 classes was used to verify the validity of the MKs-ELM-DNN model proposed. According to the results obtained, the accuracy score was obtained as 98.36% using the MKs-ELM-DNN model. The results have demonstrated that, when compared, the MKs-ELM-DNN model proposed is proven to be more successful than the state-of-the-art algorithms and previous studies. CONCLUSION: This study shows that the proposed Multiple Kernels-ELM-based Deep Neural Network model can effectively contribute to the identification of COVID-19 disease. äCOVID-19 Detection System Using Chest CT Images and Multiple Kernels-E(epmc).pdf DeepDyve Pubget Overpricing