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10.1016/j.ijleo.2021.167199 http://scihub22266oqcxt.onion/10.1016/j.ijleo.2021.167199 34028466!8130607!34028466     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Optik+(Stuttg) 2021 ; 241 (ä): 167199 Nephropedia Template TP {{tp|p=34028466|t=2021. Diagnosing COVID-19 disease using an efficient CAD system |pdf=|usr=}}{{34028466}} gab.com Text Diagnosing COVID-19 disease using an efficient CAD system JO: Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=34028466 #FOAvip Todays, COVID-19 has caused much death and its spreading speed is increasing, regarding virus mutation. This outbreak warns diagnosing infected people is an important issue. So, in this research, a computer-aided diagnosis (CAD) system called COV-CAD is proposed for diagnosing COVID-19 disease. This COV-CAD system is created by a feature extractor, a classification method, and a content-based imaged retrieval (CBIR) system. The proposed feature extractor is created by using the modified AlexNet CNN. The first modification changes ReLU activation functions to LeakyReLU for increasing efficiency. The second change is converting a fully connected (FC) layer of AlexNet CNN with a new FC, which results in reducing learnable parameters and training time. Another FC layer with dimensions 1 x 64 is added at the end of the feature extractor as the feature vector. In the classification section, a new classification method is defined in which the majority voting technique is applied on outputs of CBIR, SVM, KNN, and Random Forest for final diagnosing. Furthermore, in retrieval section, the proposed method uses CBIR because of its ability to retrieve the most similar images to the image of a patient. Since this feature helps physicians to find the most similar cases, they could conduct further statistical evaluations on profiles of similar patients. The system has been evaluated by accuracy, sensitivity, specificity, F1-score, and mean average precision and its accuracy for CT and X-ray datasets is 93.20% and 99.38%, respectively. The results demonstrate that the proposed method is more efficient than other similar studies. Twit Text FOAVip Diagnosing COVID-19 disease using an efficient CAD system ????Optik (Stuttg) http://www.kidney.de/pget.php?&tw=1&pmid=34028466 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34028466 #FOAvip English Wikipedia <ref>{{Cite pmid|34028466}}</ref> Diagnosing COVID-19 disease using an efficient CAD system #MMPMID34028466 Shakarami A; Menhaj MB; Tarrah H Optik (Stuttg) 2021[Sep]; 241 (ä): 167199 PMID34028466show ga Todays, COVID-19 has caused much death and its spreading speed is increasing, regarding virus mutation. This outbreak warns diagnosing infected people is an important issue. So, in this research, a computer-aided diagnosis (CAD) system called COV-CAD is proposed for diagnosing COVID-19 disease. This COV-CAD system is created by a feature extractor, a classification method, and a content-based imaged retrieval (CBIR) system. The proposed feature extractor is created by using the modified AlexNet CNN. The first modification changes ReLU activation functions to LeakyReLU for increasing efficiency. The second change is converting a fully connected (FC) layer of AlexNet CNN with a new FC, which results in reducing learnable parameters and training time. Another FC layer with dimensions 1 x 64 is added at the end of the feature extractor as the feature vector. In the classification section, a new classification method is defined in which the majority voting technique is applied on outputs of CBIR, SVM, KNN, and Random Forest for final diagnosing. Furthermore, in retrieval section, the proposed method uses CBIR because of its ability to retrieve the most similar images to the image of a patient. Since this feature helps physicians to find the most similar cases, they could conduct further statistical evaluations on profiles of similar patients. The system has been evaluated by accuracy, sensitivity, specificity, F1-score, and mean average precision and its accuracy for CT and X-ray datasets is 93.20% and 99.38%, respectively. The results demonstrate that the proposed method is more efficient than other similar studies. äDiagnosing COVID-19 disease using an efficient CAD system (epmc).pdf DeepDyve Pubget Overpricing