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10.1016/j.patcog.2021.108081 http://scihub22266oqcxt.onion/10.1016/j.patcog.2021.108081 34149099!8193974!34149099     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34149099&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 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit 2021 ; 119 (ä): 108081 Nephropedia Template TP {{tp|p=34149099|t=2021. Detecting pulmonary diseases using deep features in X-ray images |pdf=|usr=}}{{34149099}} gab.com Text Detecting pulmonary diseases using deep features in X-ray images JO: Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34149099 #FOAvip COVID-19 leads to radiological evidence of lower respiratory tract lesions, which support analysis to screen this disease using chest X-ray. In this scenario, deep learning techniques are applied to detect COVID-19 pneumonia in X-ray images, aiding a fast and precise diagnosis. Here, we investigate seven deep learning architectures associated with data augmentation and transfer learning techniques to detect different pneumonia types. We also propose an image resizing method with the maximum window function that preserves anatomical structures of the chest. The results are promising, reaching an accuracy of 99.8% considering COVID-19, normal, and viral and bacterial pneumonia classes. The differentiation between viral pneumonia and COVID-19 achieved an accuracy of 99.8%, and 99.9% of accuracy between COVID-19 and bacterial pneumonia. We also evaluated the impact of the proposed image resizing method on classification performance comparing with the bilinear interpolation; this pre-processing increased the classification rate regardless of the deep learning architectures used. We c ompared our results with ten related works in the state-of-the-art using eight sets of experiments, which showed that the proposed method outperformed them in most cases. Therefore, we demonstrate that deep learning models trained with pre-processed X-ray images could precisely assist the specialist in COVID-19 detection. Twit Text FOAVip Detecting pulmonary diseases using deep features in X-ray images ????Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=34149099 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34149099 #FOAvip English Wikipedia <ref>{{Cite pmid|34149099}}</ref> Detecting pulmonary diseases using deep features in X-ray images #MMPMID34149099 Vieira P; Sousa O; Magalhaes D; Rabelo R; Silva R Pattern Recognit 2021[Nov]; 119 (ä): 108081 PMID34149099show ga COVID-19 leads to radiological evidence of lower respiratory tract lesions, which support analysis to screen this disease using chest X-ray. In this scenario, deep learning techniques are applied to detect COVID-19 pneumonia in X-ray images, aiding a fast and precise diagnosis. Here, we investigate seven deep learning architectures associated with data augmentation and transfer learning techniques to detect different pneumonia types. We also propose an image resizing method with the maximum window function that preserves anatomical structures of the chest. The results are promising, reaching an accuracy of 99.8% considering COVID-19, normal, and viral and bacterial pneumonia classes. The differentiation between viral pneumonia and COVID-19 achieved an accuracy of 99.8%, and 99.9% of accuracy between COVID-19 and bacterial pneumonia. We also evaluated the impact of the proposed image resizing method on classification performance comparing with the bilinear interpolation; this pre-processing increased the classification rate regardless of the deep learning architectures used. We c ompared our results with ten related works in the state-of-the-art using eight sets of experiments, which showed that the proposed method outperformed them in most cases. Therefore, we demonstrate that deep learning models trained with pre-processed X-ray images could precisely assist the specialist in COVID-19 detection. äDetecting pulmonary diseases using deep features in X-ray images (epmc).pdf DeepDyve Pubget Overpricing