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

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.

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.

Source code of paper "Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans".

[AAAI2021] Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans

COVID-19 scan image detection using GraphCovidNet (GIN model)

The official implementation of the iConference 2022 paper "Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection"

The Covid-19 virus is fast spreading disease in globally, which threateness billions of human begins. In this paper, Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is introduced for Covid-19 prediction by audio signal. Here, Covid-19 prediction is done using DNFN, and it is trained by developed JHBO algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However, early and precise prediction of Covid-19 is more difficult, because of different sizes and resolutions of input image. An effective Covid-19 detection technique is introduced based on hybrid optimization driven deep learning model. The Deep Neuro Fuzzy network (DNFN) is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non Covid-19. Moreover, the DNFN is trained by devised Jaya Honey Badger Optimization (JHBO) approach, which is introduced by combining Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. An effectual Covid-19 detection model using devised Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy persons sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. This paper explicates the Covid-19 detection approach using designed Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) with audio sample. The series of steps followed for introduced Covid-19 diagnosis model are pre-processing, feature extraction, and classification. The input audio sample is acquired from a Coswara dataset and gaussian filter is applied. The gaussian filter effectively reduces the salt and pepper noise with minimal duration. Feature extraction process is most significant for precise detection of Covid-19, where spectral bandwidth, spectral roll off, Spectral flatness, Mel frequency cepstral coefficients (MFCC), spectral centroid, root mean square energy, spectral contract, and zero crossing rate are extracted. The Deep learning approach is effectual for disease detection and classification process in medical field. Here, DNFN is utilized for detecting the Covid-19 disease. Moreover, DNFN is trained by developed JHBO approach for obtaining better performance. The proposed JHBO algorithm is newly devised by combining Jaya algorithm and HBA. Here, Jaya algorithm is incorporated with HBA for obtaining improved performance with better convergence speed. The performance of DNFN is estimated with three performance metrics, namely specificity, testing accuracy and sensitivity. The proposed JHBO-based DNFN achieved improved performance testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

The COVID-19 (coronavirus) is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was first identified in mid-December 2019 in the Hubei province of Wuhan, China and by now has spread throughout the planet with more than 75.5 million confirmed cases and more than 1.67 million deaths. With limited number of COVID-19 test kits available in medical facilities, it is important to develop and implement an automatic detection system as an alternative diagnosis option for COVID-19 detection that can used on a commercial scale. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Computer vision and deep learning techniques can help in determining COVID-19 virus with Chest X-ray Images. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural network for image analysis and classification. In this research, we have proposed a deep convolutional neural network trained on five open access datasets with binary output: Normal and Covid. The performance of the model is compared with four pre-trained convolutional neural networkbased models (COVID-Net, ResNet18, ResNet and MobileNet-V2) and it has been seen that the proposed model provides better accuracy on the validation set as compared to the other four pre-trained models. This research work provides promising results which can be further improvise and implement on a commercial scale.

This project is about combining Bottleneck Attention Module(BAM) to Convolutional Neural Networks models and applying them to classify COVID-19 pneumonia from chest X-ray image dataset. The aim of this project is to compare and evaluate models performance based on their classification accuracy, precision, and recall.

Social distance monitoring and face mask detection system using a deep learning model has been implemented to tackle the COVID-19 situation.

The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. In this paper, the input audio samples are fed into the pre-processing module in which median filtering is done to remove the noise and artifacts from the audio samples. The feature extraction is carried out by considering features, like spectral contrast, Mel frequency cepstral coefficients (MFCC), Empirical Mode Decomposition (EMD) algorithm, spectral flux, Fast Fourier Transform (FFT), spectral roll-off, spectral centroid, Root mean square energy, zero-crossing rate, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude. Moreover, the deep Q network is applied for Covid-19 classification phase wherein the training of deep Q network is done using the proposed optimization algorithm, named Snake Jaya Honey Badger Optimization (SJHBO) algorithm. The proposed SJHBO algorithm is the hybridization of Jaya Honey Badger Optimization (JHBO) along with Snake optimization. Hence, the developed method achieved the better superior performance based on the accuracy, sensitivity and specificity .

The use of advanced artificial intelligence (AI) techniques combined with radiological imaging can be useful for accurate diagnosis of the disease and can also help overcome the shortage of specialist physicians in remote villages. In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented. The proposed model is designed to provide an accurate diagnosis for binary classification (COVID vs. pneumonia ) and multi-classification (covid, pneumonia, nodel, boronshit, normal). Our model produces 99.08% classification accuracy for binary classifications and 95.02% for multi-class cases. The DarkNet model was used in our study as a classification where you only look at the real-time object recognition system once (YOLO(v3)). We applied 17 layers of the convolution and applied different filters on each layer. Our model can be used to help radiologists discredit their initial screening and can also be used over the cloud for rapid screening of patients.

"Detection of Covid-19 & Pneumonia” is a desktop application which utilizes moderate desktop system. System will be using machine learning algorithms, which will help to detect to “covid-19 & Pneumonia” using chest “x-ray images”. It is a real time disease detection system. Due to current situation around the world, most of the people are suffering from “Covid-19” and “Pneumonia”. The system will be used for the identification of the effected one’s. The main purpose of our project is to develop a system that will identify the patients either they are suffering from “Covid-19” or “Pneumonia”. In the current situation the practices taking place for the detection of “Covid-19” & “Pneumonia” includes hardware which is quite expensive and out of reach for the normal use. Hardware required qualified person to operate it. The knowledge and implementation on this current scenario is quite limited. This system will be in reach for an ordinary person, which will not require any expertise to operate it. Our system will detect two different diseases in real time. This system is based on recognition of “Covid-19” and “Pneumonia” on the basis of “Chest X-ray” images. This system is hardware free which makes it useful for every patient, and will evaluate efficient results. Keeping for hardware free this system will be accessible and easily available for the usage, hence no special qualified operator is required to operate it. This system is developed in spyder using python and Convolutional Neural Network (CNN). System testing, load testing, compatibility testing and integration testing techniques are performed on this system. Quality, accuracy, performance and consistency is checked through these testing techniques. All the modules containing image loading, model saving, detection, report generating are working perfectly. No significant errors are found during the testing phase. It is only limited to chest x-rays as an input images. As it is a desktop application, which will only work on desktop and windows environment. It requires only digital images of chest x-rays as an input. As future work, to overcome these limitations we can add the variety of input types. We can deploy this system on different platforms like web and android.

Distanced - AI-based Detection Model to ensure Social Distancing during COVID-19 Pandemic

SARS-CoV-2 (Covid-19) detection from lung CT-scans using traditional feature extraction through application of SVM, MLP and KNN models.

To develop a “Face mask detection” software using Python Programming Language & DataScience, to facilitate easy mask detection from real time video stream as well as from image. About Module: The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision, data science and deep learning.

Detection and monitoring of crowds using computer vision has applications in crowd management and surveillance. Crowd management is important for public safety, especially now amidst the COVID-19 pandemic. Computer vision algorithms can assist with social distancing efforts aimed at slowing the spread of the virus, and alert when violations on the permitted headcount within a space occur. Crowd counting and localization can also be useful when designing public spaces such as airports and malls, and in making decisions on how to manage crowds in these public spaces. Team Members Tasneem Naheyan Kenan Li Inder Dhillon Jing Li Sadman Sakib Reza Karimi Youssef Guirguis Data We used the WILDTRACK dataset for this project. Repo Structure Different components of the project are divided into seperate branches: wildtrack_dataset contains the homography transformation and distance calulation functions. YOLO branch contains the YOLOv3 model code. evaluation branch contains the code to run the evaluation metrics. Pre-generated YOLO detections provided in .pkl files. Kernel_Density_Estimation contains the KDE code. main branch contains the script to generate location position predictions and saves them to .pkl files.

intelligent MRI machine: disease predection and screening¶ general presentation of the project Following an absence of medication for certain diseases, it is always necessary to provide them at a precausal stage in order to increase the chances of recovery. This is why we thought of creating a Detection and screening model from imaging for example if a patient consults a rheumatologist for a skull fracture and the latter recommends an MRI our program is able to detect a tumor at the level of the brain if it exists. Not only that, MRIs do not generally provide all the information sufficient to make the diagnosis! Our program will lend a hand to help: The radiologist: to recommend a specialist medcin: for a better diagnosis patient: for early detection of illness that's why we collected a dataset from several datasets such as brain-tumor, alzheimer, covid_19, chest tumor (due to the time constraint we just collected a dataset of MRI images of 4 different types of disease but we can give a huge database of all diseases since the algorithm has shown these performances) so that the MRI can detect all types of diseases in an automatic way so a patient will be able to make a general diagnosis without the intervention of the doctor after a few minutes. then we built the deep learning model based on CNN then we built the human machine interface which will be implemented on the MRI scanner

Single shot model, COVID-19 prediction + lesion detection/segmentation

Develop a COVID-19 detection model using a pre-trained Densenet121 model.

Covid-19 X-Ray Detection deep learning model using Tensorflow and Keras

Implementation of the paper “An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification.” Reproduces the proposed architecture and evaluation for automated COVID-19 detection.

Intervention in Health Misinformation Using Large Language Models for Automated Detection, Thematic Analysis, and Inoculation: Case Study on COVID-19

Training a model for surgical mask detection in the context of the COVID-19. It is a binary classification task in which an audio file must be labeled as without mask or with mask.

Repository for the manuscript entitled: From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates. by Marlena M Siwiak, Pawel Szczesny, and Marian P Siwiak

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments acrossthe world to impose lockdownsto prevent virustransmissions. Reportsindicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images , we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal isto identifywhether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning.

Healthcare is the most important topic in society .It tries to find the effective and robust detection as soon as possible to patient to get the appropriate care .It becomes very necessary in the medical field to detect the particular disease. The large no of techniques are used ,going beyond the traditional methods. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects to find and make the detection we are making the project dealing with the Machine Learning applied to the diagnosis of Covid diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this we have used the more than 20000 X-Ray images to train the model to get the best results of prediction. We have Images for Corona positive , Normal , Lungs Infection and viral Pneumonia . By using the Best Deep Learning model we have trained our model and we made the model for prediction .By using Transfer Learning we build our model. We have also added the self assessment Covid-19 form for the patient.

In this Project we aim to dive into a Present Societal Pandemic issue which we are facing around us past 2 years due to outbreak of Novel Corona Virus.  Getting tested for covid-19 virus is not an easy deal with costly RT-PCR test, and delayed results, and with its no. of variants with different mutations emerging everyday all the new methods found to detect the virus and its variant have either become : - Ineffective as all tests may not find each of the variants. - Each of them a set a finical restrictions for the technology used. - Each test has its own detection time .  Chest X-Ray already exists and overcomes most of the above drawbacks, but still fail to give long term effects or severity.  So as a Solution We Aim to develop a model to give large no. of classifications and comparisons of Covid Patients and whether it leads to pneumonia disease , also these models could be trained to classify long term effects after years of infection how things could change w.r.t chest infections, and lead to other chronic disease.