New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis
This work offers an incremental improvement in diagnostic accuracy and speed for COVID-19 detection from chest x-rays, which is significant for medical professionals needing rapid and reliable screening.
The paper addresses the challenge of differentiating COVID-19 from other pneumonia in chest x-ray images by proposing a new Bag of Deep Visual Words (BoDVW) method. This method modifies the standard BoVW by removing feature map normalization and adding deep feature normalization, leading to stable and prominent classification accuracy with shorter computation times compared to state-of-the-art methods.
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.