Face Mask Detection using NVIDIA Transfer Learning Toolkit (TLT) and DeepStream for COVID-19

1st place solution for SIIM-FISABIO-RSNA COVID-19 Detection Challenge

Valencia Region Image Bank (BIMCV) that combines data from the PadChest dataset with future datasets based on COVID-19 pathology to provide the open scientific community with data of clinical-scientific value that helps early detection of COVID-19

COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.

COVID-Net Open Source Initiative - Models and Data for COVID-19 Detection in Chest CT

Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-Ray Images

The implementation of "A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT"

This repository is designated to detecting trucks using Sentinel-2 data.

Real-time video streaming mask detection based on Python. Designed to defeat COVID-19.

✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation

Official repository for data set and baselines for covid19 fake news data.

COVID-19 Detection Using Chest X-Ray

Fully automated code for Covid-19 detection from CT scans from paper: https://doi.org/10.1016/j.bspc.2021.102588

A Vision-based Social Distancing and Critical Density Detection System for COVID-19

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early prediction new Coronavirus dissease infections in some of the mentioned affected countries (India, USA, Czech Republic and Russia) , which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempt has been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.

Covid-19 detection in chest x-ray images using Convolution Neural Network.

Detect Covid-19 with Chest X-Ray Data

COVID-Next -> Pytorch upgrade of the COVID-Net for COVID-19 detection in X-Ray images

This project uses Deep learning concept in detection of Various Deadly diseases. It can Detect 1) Lung Cancer 2) Covid-19 3)Tuberculosis 4) Pneumonia. It uses CT-Scan and X-ray Images of chest/lung in detecting the disease. It has a Accuracy between 50%-80%. It can take input in any Image format or through Live videos and provide accurate output results.

Covid-19 Detection Experiments

A novel dataset containing over 15 Million COVID-19 vaccine-related tweets and 15 Thousand labeled tweet for vaccine misinformation detection

COVID-19 Mask Detection from Faces using CNN

The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.

Official Repository for A Novel Approach for detecting Normal, COVID-19 and Pneumonia patient using only binary classifications from chest CT-Scans Paper

Here, I created my own deep learning(CNN) model for early detection of COVID-19 from chest x-ray images. If we were to answer the question that why we need a deep learning model for early detection of COVID-19 from chest x-ray images, we can say the followings, doctors have seen that even if the test kits desined for diagnosis results in negative, the real results are positive for some patients when they review the chest X-ray images. For now the public dataset contains less amount of data which you can see in the dataset2 folder. We get this dataset from open-source https://github.com/ieee8023/covid-chestxray-dataset, but for sure it is not enough to train a proper deep learning model. But just to show that how easy it is to create an AI for the early detection of these kind of viruses. Just keep in mind that this cannot be used for diagnosis without training many more images in high-resolution and professinal medical tests. There you go! Let's work together to fight against COVID-19. As a tool, I used Keras with Tensorflow background, and the model can be improved by addig more convolution and pooling layers, and increasing the number of feature detectors'. Don't forget to upvote. Best Regards.

The entire workflow of developing deep learning model for detecting face mask.

Code for paper "AI for radiographic COVID-19 detection selects shortcuts over signal"

Covid-19 and Pneumonia detection from X-ray Images from the paper: https://doi.org/10.1016/j.imu.2020.100360

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AAAI2021 COVID19-Fake-News-Detection-in-English challenge 3rd soulution