Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN

TL;DR

This paper introduces a novel deep learning model with a channel attention mechanism for accurate COVID-19 detection in CT scans, achieving high accuracy and outperforming existing methods.

Contribution

A modified Xception model with a new channel attention module and weighted pooling for improved COVID-19 CT classification accuracy.

Findings

Achieved 96.99% classification accuracy.

Outperformed state-of-the-art techniques.

Demonstrated effectiveness of attention mechanisms in medical imaging.

Abstract

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.