COVID-19
We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 hours of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm).
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This datasets contains Xrays of positive COVID-19 and Pneumonia patients.
For the COVID-19 class, three sources were used in this work, BIMCV-COVID-19+ (Spain), COVID-19- AR (USA) and V2-COV19-NII (Germany).
The pneumonia class data came from 3 sources: (i) the National Institute of Health (NIH) dataset, (ii) Chexpert dataset and (iii) Padchest dataset.
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Lung segmentation is essential in developing AI-assisted diagnosis methods. Here is the result of lung segmentation using morphological operation, and it has been used in our study. It contains 7053 CT slices in .jpg format. And the original dataset can be seen via the Kaggle link https://www.kaggle.com/hgunraj/covidxct
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The dataset collects the results of a survey of 325 respondents. Each respondent is asked to design a route from an origin to a destination taking into account the following considerations:
- The route should avoid crowds to avoid getting COVID-19.
- They should take into account the context provided: day, time, month, holiday period.
A total of 10 scenarios located in the city of Ciudad Real were designed.
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The Ways To Wear a Mask or a Respirator Database (WWMR-DB) is a test database that can be used to compare the behavior of current mask detection systems with images that most closely resemble the real case. It consists of 1222 images divided into 8 classes, depicting the most common ways in which masks or respirators are worn:
- Mask Or Respirator Not Worn
- Mask Or Respirator Correctly Worn
- Mask Or Respirator Under The Nose
- Mask Or Respirator Under The Chin
- Mask Or Respirator Hanging From An Ear
- Mask Or Respirator On The Tip Of The Nose
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This dataset has been developed based on the work of the GeoCOV19Tweets Dataset. The original work by Lamsal, R. runs network analysis on a similar dataset to understand the underlying relationship between countries and hashtags. The work did an analysis on roughly 300k number of [country, hashtag] relations from 190 countries and territories, and 5055 unique hashtags. This work pushes the number of relationships by 3 times.
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BIMCV-COVID19- dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of no COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
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The MCData was designed and produced for mouth cavity detection and segmentation. This dataset can be utilized for training and testing of mouth cavity instance segmentation networks. This dataset is the first available dataset for detecting and segmentation of mouth cavity main components to the best of the authors’ knowledge.
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BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
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