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10.1016/j.chaos.2021.110749 http://scihub22266oqcxt.onion/10.1016/j.chaos.2021.110749 33589854!7874964!33589854     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chaos+Solitons+Fractals 2021 ; 145 (ä): 110749 Nephropedia Template TP {{tp|p=33589854|t=2021. Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images |pdf=|usr=}}{{33589854}} gab.com Text Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images JO: Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=33589854 #FOAvip Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 +/- 0.02 and a sensitivity of 0.987 +/- 0.05, 0.963 +/- 0.05, and 0.973 +/- 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In summary, the results obtained are stable over independent cohorts and interpretable using Grad-CAM localizations that serve as clinical evidence. Twit Text FOAVip Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images ????Chaos Solitons Fractals http://www.kidney.de/pget.php?&tw=1&pmid=33589854 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33589854 #FOAvip English Wikipedia <ref>{{Cite pmid|33589854}}</ref> Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images #MMPMID33589854 Rajpal S; Lakhyani N; Singh AK; Kohli R; Kumar N Chaos Solitons Fractals 2021[Apr]; 145 (ä): 110749 PMID33589854show ga Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 +/- 0.02 and a sensitivity of 0.987 +/- 0.05, 0.963 +/- 0.05, and 0.973 +/- 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In summary, the results obtained are stable over independent cohorts and interpretable using Grad-CAM localizations that serve as clinical evidence. äUsing handpicked features in conjunction with ResNet-50 for improved d(epmc).pdf DeepDyve Pubget Overpricing