Covid Contact Tracing Integrated with Blockchain Technology

The product to be developed in this project is concerned to be a Mobile Application for route tracing based on user's location history. Each user is tracked using the application using GPS location. If a person tests positive for covid-19 then all person in contact with that person will be notified. User can register to this app using his/her Aadhar Card number. A unique id is generated for each user's while registering in this app. So, that with this unique id only, each person is identified and their information is updated. Here blockchain is used for storing user's data. When a person registers to the system their information will be stored in blockchain. Each person will get unique QR code so that when this person enters a store or organization they can scan QR code and get information about the user. The information about the containment Zone will be updated and if the person is coming from containment zone, organization can know about that. A machine learning tool is implemented to detect covid-19 from user's chest CT-scan image. User can upload image and check whether they have covid-19. Resnext network will be used for machine learning. Resnext is the neural network architecture for image classification. © 2022 IEEE.

Multi-class Classification of COVID-19 in Chest X-Ray Using Deep Convolutional Neural Networks

COVID-19 has principally affected everybody within the world in a way or another and thousands of individuals are becoming infected daily. In Present ways for checking COVID positive or negative, is taking a lot of time for results and these results are giving low specificity and sensitivity. Because of that the computer science—Artificial Intelligence (AI) is necessary in finding the positive COVID-19 cases. With Image processing and machine learning and deep learning techniques the researchers are able to achieve high accuracy and sensitivity and specificity from Chest X-ray (CXR) and Computed tomography (C.T) images. In this paper, we have proposed different deep neural networks like CNN, Alexnet, ResNet, Inception-v3 and ResNeXt-101-32x8d (all of those belong to the CNN family) with around 20,000+ CXR pictures of 3 classes. CXR is the initial technique which is important in diagnosing the Covid-19 patients. For verifying the strength of the models we compared validation accuracies, inception V3 achieved the best accuracy of 95%, however, we must always conjointly take into account the training time and complexity of the model. When the models accuracy, specificity, and sensitivity are higher, then it is really helpful for non-radiologist medical staff to diagnoses and quarantine faster when hospitals are flooded with patients, It reduces screening time for COVID-19 greatly. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

CT image segmentation of COVID-19 based on UNet++ and ResNeXt

The novel coronavirus pneumonia (COVID-19) pandemic is spreading globally. Computerized tomography (CT) imaging technology plays a vital role in the fight against global COVID-19. When diagnosing new coronary pneumonia, it will be helpful if the new coronary pneumonia focus area can be automatically and accurately segmented from the CT image The doctor makes a more accurate and quick diagnosis. Aiming at the segmentation problem of neo-coronary pneumonia lesions, an automatic segmentation method based on the improved U-Net++ model is proposed. The ResNeXt network pre-trained on ImageNet is used in the encoder and decoder to extract features of effective information. The experimental results on the public data set show. The mIou, mPA, and loss of the proposed algorithm are 81.67%, 87.78%, and 0.0145 respectively. Compared with other semantic segmentation algorithms, this method can effectively segment the neo-coronary pneumonia lesion area and has good segmentation performance. © 2021 IEEE.

COVIZONE: A Deep Transfer Learning-Based System for Automatic Detection of Covid-19 using Chest X-Ray

With increasing rise of COVID-19 infected patients in India and worldwide, examining and detecting COVID-19 among such large number of populations is becoming a humongous task for the medical practitioners and civic authorities. RT-PCR, real time reverse transcription-polymerase chain reaction technique is widely accepted and one of the reliable methods for detection of novel COVID-19.However, being a time consuming, laborious and expensive method for declaring results for the patients in over 6-8 hours to even 3 days in remote places, this technique is not being widely used. The high and very fast spread rate of COVID-19 and low availability of RT-PCR kit, is making the use of computer assisted technologies an inevitable and a potentially faster response mechanism catering to a large population with least human error and a cost-effective solution. Therefore, an intelligent system COVIZONE has been presented, in the proposed work, designed using state of the art pre-trained CNN model to analyze and detect COVID-19 presence in the lungs using Chest X-Ray and CT-Scan Images. In the proposed work, a multi-class classification (Normal, Pneumonic and COVID-19) of patients using ResNet and ResNext CNN model has been done. Both the models show similar performance with high accuracy of 96% and 97% respectively on public dataset of COVID-19, Pneumonia and Normal CXR and CT-Scans. To avoid skewness due to lesser number of COVID-19 CXR images, dataset used has limited Pneumonia and Normal CXR images to train the system and achieved noticeable high accuracy. The proposed COVID-19 detection model i.e. COVIZONE, even if not used as a primary Covid testing and detection tool, can still be a very helpful tool for screening potentially infected persons and help the physicians who are yet not trained for this pandemic diagnosis. © 2021 IEEE

Deep learning based detection and analysis of COVID-19 on chest X-ray images.

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.