A lightweight CORONA-NET for COVID-19 detection in X-ray images.

Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors.

SARS-CoV-2 Detection Using Optical Fiber Based Sensor Method.

The SARS-CoV-2 Coronavirus disease, also known as the COVID-19 pandemic, has engendered the biggest challenge to human life for the last two years. With a rapid increase in the spread of the Omicron variant across the world, and to contain the spread of COVID-19 in general, it is crucial to rapidly identify this viral infection with minimal logistics. To achieve this, a novel plastic optical fiber (POF) U-shaped probe sensing method is presented for accurate detection of SARS-CoV-2, commonly known as the COVID-19 virus, which has the capability to detect new variants such as Omicron. The sample under test can be taken from oropharyngeal or nasopharyngeal via specific POF U-shaped probe with one end that is fed with a laser source while the other end is connected to a photodetector to receive the response and postprocess for decision-making. The study includes detection comparison with two types of POF with diameters of 200 and 500 µm. Results show that detection is better when a smaller-diameter POF is used. It is also seen that the proposed test bed and its envisaged prototype can detect the COVID-19 variants within 15 min of the test. The proposed approach will make the clinical diagnosis faster, cheaper and applicable to patients in remote areas where there are no hospitals or clinical laboratories due to poverty, geographic obstacles, or other factors.