  In recent times, the use of chest Computed Tomography (CT) images for
detecting coronavirus infections has gained significant attention, owing to
their ability to reveal bilateral changes in affected individuals. However,
classifying patients from medical images presents a formidable challenge,
particularly in identifying such bilateral changes. To tackle this challenge,
our study harnesses the power of deep learning models for the precise
classification of infected patients. Our research involves a comparative
analysis of deep transfer learning-based classification models, including
DenseNet201, GoogleNet, and AlexNet, against carefully chosen supervised
learning models. Additionally, our work encompasses Covid-19 classification,
which involves the identification and differentiation of medical images, such
as X-rays and electrocardiograms, that exhibit telltale signs of Covid-19
infection. This comprehensive approach ensures that our models can handle a
wide range of medical image types and effectively identify characteristic
patterns indicative of Covid-19. By conducting meticulous research and
employing advanced deep learning techniques, we have made significant strides
in enhancing the accuracy and speed of Covid-19 diagnosis. Our results
demonstrate the effectiveness of these models and their potential to make
substantial contributions to the global effort to combat COVID-19.
