  The rapid spread of COVID-19 has necessitated efficient and accurate
diagnostic methods. Computed Tomography (CT) scan images have emerged as a
valuable tool for detecting the disease. In this article, we present a novel
deep learning approach for automated COVID-19 CT scan classification where a
modified Xception model is proposed which incorporates a newly designed channel
attention mechanism and weighted global average pooling to enhance feature
extraction thereby improving classification accuracy. The channel attention
module selectively focuses on informative regions within each channel, enabling
the model to learn discriminative features for COVID-19 detection. Experiments
on a widely used COVID-19 CT scan dataset demonstrate a very good accuracy of
96.99% and show its superiority to other state-of-the-art techniques. This
research can contribute to the ongoing efforts in using artificial intelligence
to combat current and future pandemics and can offer promising and timely
solutions for efficient medical image analysis tasks.
