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10.1016/j.compbiomed.2021.104401 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104401 34010794!8058056!34010794     free     free     free 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 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 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Biol+Med 2021 ; 134 (ä): 104401 Nephropedia Template TP {{tp|p=34010794|t=2021. Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images |pdf=|usr=}}{{34010794}} gab.com Text Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=34010794 #FOAvip Novel Coronavirus is deadly for humans and animals. The ease of its dispersion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers, and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radiologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community. Twit Text FOAVip Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=34010794 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34010794 #FOAvip English Wikipedia <ref>{{Cite pmid|34010794}}</ref> Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images #MMPMID34010794 Ahmad F; Ghani Khan MU; Javed K Comput Biol Med 2021[Jul]; 134 (ä): 104401 PMID34010794show ga Novel Coronavirus is deadly for humans and animals. The ease of its dispersion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers, and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radiologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community. |*COVID-19[MESH] |*Deep Learning[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]Deep learning model for distinguishing novel coronavirus from other ch(epmc).pdf DeepDyve Pubget Overpricing