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10.1371/journal.pone.0250688 http://scihub22266oqcxt.onion/10.1371/journal.pone.0250688 33974652!8112662!33974652     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 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 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       PLoS+One 2021 ; 16 (5): e0250688 Nephropedia Template TP {{tp|p=33974652|t=2021. COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi |pdf=|usr=}}{{33974652}} gab.com Text COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33974652 #FOAvip The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods. Twit Text FOAVip COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33974652 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33974652 #FOAvip English Wikipedia <ref>{{Cite pmid|33974652}}</ref> COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi #MMPMID33974652 Hosny KM; Darwish MM; Li K; Salah A PLoS One 2021[]; 16 (5): e0250688 PMID33974652show ga The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods. |*Algorithms[MESH] |*Tomography, X-Ray Computed[MESH] |Area Under Curve[MESH] |COVID-19/*diagnosis/virology[MESH] |Deep Learning[MESH] |Humans[MESH] |ROC Curve[MESH] |SARS-CoV-2/isolation & purification[MESH]COVID-19 diagnosis from CT scans and chest X-ray images using low-cost(epmc).pdf DeepDyve Pubget Overpricing