Attention-based VGG-16 model for COVID-19 chest X-ray image classification

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 5; pp. 2850 - 2863
• Main Authors: Sitaula, Chiranjibi, Hossain, Mohammad Belayet
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial Intelligence Applications for COVID-19; Chest; Classification; Computed tomography; Computer Science; Control; Convolution; Coronaviruses; COVID-19; Deep learning; Detection; Diagnosis; Image classification; Machine learning; Machines; Manufacturing; Mechanical Engineering; Medical imaging; Modules; Performance evaluation; Polymerase chain reaction; Prediction; Processes; COVID-19; Deep learning; SARS-CoV2; Chest x-rays; Classification
• ISSN: 0924-669X; 1573-7497; 1573-7497
• DOI: 10.1007/s10489-020-02055-x

Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is one of the cheapest alternative options to diagnose the early stage of COVID-19 disease compared to other alternatives such as Polymerase Chain Reaction (PCR), Computed Tomography (CT) scan, and so on. To this end, there have been few works proposed to diagnose COVID-19 by using CXR-based methods. However, they have limited performance as they ignore the spatial relationships between the region of interests (ROIs) in CXR images, which could identify the likely regions of COVID-19’s effect in the human lungs. In this paper, we propose a novel attention-based deep learning model using the attention module with VGG-16. By using the attention module, we capture the spatial relationship between the ROIs in CXR images. In the meantime, by using an appropriate convolution layer (4th pooling layer) of the VGG-16 model in addition to the attention module, we design a novel deep learning model to perform fine-tuning in the classification process. To evaluate the performance of our method, we conduct extensive experiments by using three COVID-19 CXR image datasets. The experiment and analysis demonstrate the stable and promising performance of our proposed method compared to the state-of-the-art methods. The promising classification performance of our proposed method indicates that it is suitable for CXR image classification in COVID-19 diagnosis.