New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis

TL;DR

This paper introduces a novel Bag of Deep Visual Words (BoDVW) feature extraction method that improves COVID-19 diagnosis from chest x-rays by better capturing semantic information, leading to higher accuracy and faster computation.

Contribution

The paper proposes a new BoVW-based feature extraction technique, BoDVW, that enhances COVID-19 x-ray classification by preserving semantic features and reducing computation time.

Findings

BoDVW features improve classification accuracy for COVID-19 detection.

The method achieves faster results compared to existing approaches.

BoDVW effectively differentiates COVID-19 from other pneumonia in x-ray images.

Abstract

Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the pneumonia-like effect in the lungs, the examination of chest x-rays can help to diagnose the diseases. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features may not be appropriate to represent chest x-rays as they have a few semantic regions. Though the Bag of Visual Words (BoVW) based features are shown to be more appropriate for x-ray type of images, existing BoVW features may not capture enough information to differentiate COVID-19 infection from other pneumonia-related infections. In this paper, we propose a new BoVW method over deep features, called Bag of Deep Visual Words (BoDVW), by removing the feature map normalization step and adding deep features normalization step on the raw feature maps. This helps to preserve the semantics of each feature map that may have important clues to differentiate COVID-19 from pneumonia. We evaluate the effectiveness of our proposed BoDVW features in chest x-rays classification using Support Vector Machine (SVM) to diagnose COVID-19. Our results on a publicly available COVID-19 x-ray dataset reveal that our features produce stable and prominent classification accuracy, particularly differentiating COVID-19 infection from other pneumonia, in shorter computation time compared to the state-of-the-art methods. Thus, our method could be a very useful tool for quick diagnosis of COVID-19 patients on a large scale.