Prediction of COVID 19 pandemic using convolutional neural network and compare accuracy with artificial neural network

This research tests two neural network types—artificial neural networks and convolutional neural networks—to see whether one is better at predicting the spread of COVID-19. We evaluated the performance of Convolutional A Team Approach to COVID-19 Pandemic Forecasting with the Use of ML and CNNs using two sets of 20 samples. G power, which had an 80% pretest power, was used to compute the sample size. Data models were developed using Chest X-rays from healthy individuals and COVID-19 patients were used in conjunction with artificial neural network (ANN) and convolutional neural network (CNN) methods. The results show that CNN has a 98.5% accuracy rate and ANN 95.33% accuracy rate, with a p-value less than 0.05 indicating statistical significance. When compared with the Artificial Neural Network method, the Convolutional Neural Network method yields better results in terms of accuracy.