Design of an Efficient Deep Learning Framework for Covid-19 Image Classification

The worldwide health crisis caused by COVID-19 and the subsequent control measures have had significant and widespread impacts on various aspects of human life. Detecting COVID-19 early is crucial for effective diagnosis, and ML algorithms have played a vital role in speeding up the process and improving efficiency. However, factors like delivery time and access to training data are also crucial considerations. Extensive analysis of lung ultrasound pictures using the COVID-19 database and deep learning algorithms show that the former is superior to the latter in terms of illness diagnosis. Without specialised testing, it might be difficult to tell COVID-19 from other viral fevers. The research suggests a method of COVID-19 image classification using Convolutional Neural Networks (CNNs). Further evidence of the superiority of the proposed model is provided by a loss and accuracy comparison study.