Attention Based Residual Network for Effective Detection of COVID-19 and Viral Pneumonia

Abstract

The current coronavirus (COVID-19) pandemic has led us to the healthcare, global poverty and socioeconomic crisis. One of the most significant task in this pandemic is to accurately and efficiently diagnose the COVID-19 patients and to monitor them to make prompt decisions and take appropriate actions for their monitoring, management and treatment. The early diagnosis of COVID-19 was a very troublesome and difficult challenge that CAD (Computer-Aided Diagnosis) methods successfully tackled. The CXR (chest X-ray) method proved to be a very low-cost and effective alternative to Computed Tomography (CT) scan and Real Time Polymerase Chain Reaction (RT-PCR) test, which were previously the most commonly used methods for COVID-19 diagnosis. Till now, very few CAD based techniques have been proposed to effectively detect COVID-19, but their efficiency is limited due to a number of factors. In this study, we have proposed a deep learning model using the Convolutional Block Attention Module with ResNet32. For training the model, Kaggle's dataset containing CXR images has been used. The dataset contains a total of 3886 images. Moreover, 64% of data has been used for training, 20% for testing and 16% for validation. We have experimented with different CNN architectures with different approaches like Transfer Learning, Data Augmentation and attention module. With 97.69% accuracy, the ResNet32 with attention module outperformed other architectures and approaches, improving the baseline network efficiency. This promising and efficient classification accomplishment of our proposed methodology demonstrates that it is well suited for CXR image classification in COVID-19 diagnosis in terms of both accuracy and cost.