Early detection of COVID-19 using deep learning architectures: CNN and resnet-101

The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.

Fine-Tuning ResNet-152V2 for COVID-19 Recognition in Chest Computed Tomography Images

The epidemic Covid-19 has extended to majority of nations. This pandemic is due to a contagious condition 'SARS-CoV-2', was identified by the the International Health association. In order to diagnosis this virus from 2D chest computed tomography (CT) images, we applied three different transfer learning algorithms: $VGG-19, ResNet-152V2$ and a Fine-Tuned version of $ResNet-152V2$. The different transfer learning models are used on three hundred and four exams where 74 are normal cases, 60 are community-acquired pneumonia (CAP) cases and 169 were confirmed corona-virus cases. The best accuracy value is reached by the fine-tuned $ResNet-152v2$ by 75% against 70% for the basic $ResNet-152v2$ and 66% for the $VGG-19$. © 2022 IEEE.

COVID-19 Detection and Remote Tracking System Using IoT-Based Wearable Bracelet

A new type of virus was discovered in China in the year 2019, known as COVID-19. One of the main symptoms that are easy to spot is high body temperature. The recent virus outbreak necessitates infrared thermometers used for thermal screening at public places to test the body temperature. However, this protection method still lacks because it requires a significant amount of time to monitor large numbers of people's body temperatures. Moreover, direct contact with people infected with coronavirus may spread it to the person doing the screening. In addition, this method cannot detect the infection early without visiting the infected person to a screening place. This study proposed a new system for automatically detecting the coronavirus in early time through the body temperature with no human interactions using IoT-based wearable bracelets. The body temperature sensor is integrated into the wearable bracelet with IoT technology for monitoring the body temperature and reading the current bodily temperature. The system is additionally equipped with a GPS module. It can capture the location of the person automatically. Suppose the person is suffering from high body temperature. In that case, the system will send it with location through Wi-Fi module or GSM module over the internet to cloud database and notify medical officer at the same moment to do the immediate procedures for that person. Health officers use smartphone applications for monitoring and remote tracking using the application map. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images*

With the start of 2020, the world witnessed the spread of Coronavirus disease (COVID-19). We aim in this work to employ artificial intelligence (AI) to develop a computeraided diagnosis system (CAD) in order to automatically detect COVID-19 cases and differentiate them from normal and community-acquired pneumonia (CAP) cases through the use of lung Computed Tomography (CT) images and then evaluate its performance. Deep residual learning offers a wide variety of algorithms that helps in classification problems. We apply in this work a ResNet50 based model to recognize Covid-19 cases. Extensive analysis based on an international dataset (24256 images of 304 patients) proved that the ResNet50-optimized model can recognize COVID-19 through the use of CT images with 82% accuracy, 90% recall, 65% precision, and 76% of F1.Score.