Showing results for COVID-19 PNG
GitHub Repo
https://github.com/rjbooth88/covid-19
rjbooth88/covid-19
R code and/or .png graphics for epidemiology of CoVid-19
GitHub Repo
https://github.com/ricardotran92/COVID19_Pneumonia_Normal_Chest_Xray_PA_CLAHE_medianF3x3_png
ricardotran92/COVID19_Pneumonia_Normal_Chest_Xray_PA_CLAHE_medianF3x3_png
No repository description available.
GitHub Repo
https://github.com/rico-png/git-github.com-rico-png-rico-png-Nigeria-COVID-19-Data-Analysis-Using-Python
rico-png/git-github.com-rico-png-rico-png-Nigeria-COVID-19-Data-Analysis-Using-Python
No repository description available.
GitHub Repo
https://github.com/emirkabal/covid-19
emirkabal/covid-19
Bu proje https://covid19.saglik.gov.tr/ sitesine ulaşarak COVID-19 virüsünün istatistiklerini png dosyasına döker.
GitHub Repo
https://github.com/mmehtafenil/PredictCovid19
mmehtafenil/PredictCovid19
A responsive web app connected with a deep learning model built completely on Python where users can upload JPEG, JPG, or PNG chest X-Ray and get results in a few seconds whether they are Covid-19 Positive or not.
GitHub Repo
https://github.com/LukeHebert/covid19_owid_cartograms
LukeHebert/covid19_owid_cartograms
R scripts that generate indexed PNGs that, when combined, create a video displaying some preventative measure as country color and some health outcome as country size, all over time.
GitHub Repo
https://github.com/yogipi/Classification-of-Pneumonia-and-COVID-19-Using-Deep-Residual-Network
yogipi/Classification-of-Pneumonia-and-COVID-19-Using-Deep-Residual-Network
The train and validation dataset that I used came from research conducted by Daniel S. Kermany, Michael Goldbaum, et al. and research conducted by Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, et al. Meanwhile, for the test dataset obtained from the research of Amanullah Ashraf, et al. From the dataset he shared, I took the train dataset and validated 999 x-ray images of pneumonia, 999 positive x-ray images for COVID-19, and 999 normal x-ray images. Meanwhile, for the pneumonia and COVID-19 test data, I took 333 x-ray images. The dataset I use is shared in this folder: https://drive.google.com/drive/folders/1nY6b6gYrhM4sP38vZdAvd3iHDg3nVNKV?usp=sharing The entire dataset can be viewed here: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia https://www.kaggle.com/datasets/amanullahasraf/covid19-pneumonia-normal-chest-xray-pa-dataset Raw images are different scale images in three different formats, namely .jpg, .png, and .jpeg. Raw images with different scales and formats cannot be entered into the model at the same time. So, the dataset must be preprocessed first by converting it to float by dividing each image pixel value by the maximum pixel value of the image. Using this preprocessing the image will have a similar pixel scale i.e., 224x224 with a 32-bit float type that can be entered into the system model.
GitHub Repo
https://github.com/sicwence/COVID-19-PNG
sicwence/COVID-19-PNG
Mobile app for the Covid 19 info website for PNG
GitHub Repo
https://github.com/hafizhfakhrizal/covid19scrapper
hafizhfakhrizal/covid19scrapper
This repository aims to provide database on daily COVID-19 cases in 514 districts within 34 provinces in Indonesia. The data we aim to gather are number of positive cases, recovered cases, death cases, and suspect cases of COVID-19 in each district in Indonesia. These data are available in each provinces’ dedicated COVID-19 web pages. However, the data structure in each provinces’ website varies, where some provide tables that can be scraped automatically, tables that has to be manually inputted/copied, data in the form of image (jpg/png/pdf), and spatial data (map-based dataset).
GitHub Repo
https://github.com/bgeneto/covid-19