Published Paper Details:

The COVID-19 pandemic has had a significant impact on global health, with millions of people infected & numerous deaths. The rapid identification of infected individuals and accurate diagnosis are critical to prevent the spread of the virus and provide appropriate treatment. The use of neural networks to identify disease tissues in people infected with COVID-19 has emerged as a promising approach. Neural networks can be trained to detect patterns in medical images, including those from computed tomography (CT) scans, X ray of COVID-19 patients. However, utilizing neural networks for COVID-19 diagnosis poses some significant challenges. Firstly, training data availability is limited, and acquiring labelled data requires expertise and time. Secondly, the high level of variability in COVID-19 imaging and clinical presentations can affect the neural network's accuracy. Thirdly, the neural network's interpretability and explain ability can be a challenge, making it difficult to understand how the algorithm reaches its decisions. Despite these challenges, neural networks have shown promise in identifying disease tissues in people infected with COVID-19. Neural networks have achieved high levels of accuracy in COVID-19 diagnosis, potentially reducing the need for radiologists and allowing for faster and more accurate diagnoses. As more data becomes available and neural network algorithms improve, they could play a significant role in improving COVID-19 diagnosis and treatment.