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10.1111/dth.14902 http://scihub22266oqcxt.onion/10.1111/dth.14902 33604961!7995139!33604961     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33604961&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Dermatol+Ther 2021 ; 34 (2): e14902 Nephropedia Template TP {{tp|p=33604961|t=2021. A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19 |pdf=|usr=}}{{33604961}} gab.com Text A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19 JO: Dermatol Ther http://www.kidney.de/pget.php?&tw=1&pmid=33604961 #FOAvip During the COVID-19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID-19-related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID-19-associated skin lesions from clinical images. An ensemble model of three different CNN-based algorithms was trained with clinical images of skin lesions from confirmed COVID-19 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID-19 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi-class model demonstrated an overall top-1 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID-19-rash detection were found to be 84.2 +/- 5.1% and 99.5 +/- 0.2%, respectively. The positive predictive value, NPV and area under curve values for COVID-19-rash were 88.0 +/- 5.6%, 99.4 +/- 0.2% and 0.97 +/- 0.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81 +/- 6.25%, 99.77 +/- 0.14% and 98.91 +/- 0.17%, respectively for COVID-19 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID-19-associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning-based study for automated detection of COVID-19 based on skin images and may provide a useful decision support tool for physicians to optimize contact-free COVID-19 triage, differential diagnosis of skin lesions and patient care. Twit Text FOAVip A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19 ????Dermatol Ther http://www.kidney.de/pget.php?&tw=1&pmid=33604961 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33604961 #FOAvip English Wikipedia <ref>{{Cite pmid|33604961}}</ref> A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID-19 #MMPMID33604961 Mathur J; Chouhan V; Pangti R; Kumar S; Gupta S Dermatol Ther 2021[Mar]; 34 (2): e14902 PMID33604961show ga During the COVID-19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID-19-related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID-19-associated skin lesions from clinical images. An ensemble model of three different CNN-based algorithms was trained with clinical images of skin lesions from confirmed COVID-19 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID-19 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi-class model demonstrated an overall top-1 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID-19-rash detection were found to be 84.2 +/- 5.1% and 99.5 +/- 0.2%, respectively. The positive predictive value, NPV and area under curve values for COVID-19-rash were 88.0 +/- 5.6%, 99.4 +/- 0.2% and 0.97 +/- 0.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81 +/- 6.25%, 99.77 +/- 0.14% and 98.91 +/- 0.17%, respectively for COVID-19 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID-19-associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning-based study for automated detection of COVID-19 based on skin images and may provide a useful decision support tool for physicians to optimize contact-free COVID-19 triage, differential diagnosis of skin lesions and patient care. |*COVID-19[MESH] |*Deep Learning[MESH] |*Skin Diseases/diagnosis[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Pandemics[MESH]A convolutional neural network architecture for the recognition of cut(epmc).pdf DeepDyve Pubget Overpricing