A Comparative Study of Weight Initialization Techniques for Convolutional Neural Networks in COVID-19 Classification from X-ray Images.

The rapid spread of the COVID-19 pandemic has created a pressing urgent need for accurate and efficient diagnostic tools. Recently, Convolutional neural networks (CNN) have shown great potential in classifying COVID-19 infected cases from X-ray images, but the choice of weight initialization technique plays a crucial role in their performance of the Convolutional neural networks. In this research Paper, comparative study of different weight initialization techniques COVID-19 in the context of COVID-19 classification, performance evaluation techniques have been implemented such as Glorot, Orthogonal, and Random Uniform and results shows that that the Random Uniform initialization technique outperforms other weight initialization techniques in terms of overall classification accuracy. Keywords: COVID-19, Convolutional Neural Networks, X-ray images, Weight initialization, Classification