Fusion of multi‑scale bag of deep visual words features of chest X‑ray images to detect COVID‑19 infection

Chiranjibi Sitaula, Tej Bahadur Shahi, Sunil Aryal, Faezeh Marzbanrad

Abstract:

Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19
disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these
images. Compared to other DL-based methods, the bag of deep visual words-based method
(BoDVW) proposed recently is shown to be a prominent representation of CXR images for their
better discriminability. However, single-scale BoDVW features are insufcient to capture the detailed
semantic information of the infected regions in the lungs as the resolution of such images varies in real
application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features,
which exploits three different scales of the 4th pooling layer’s output feature map achieved from VGG16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over
the 4th pooling layer using three diferent kernels: 1 × 1, 2 × 2, and 3 × 3. We evaluate our proposed
features with the Support Vector Machine (SVM) classifcation algorithm on four CXR public datasets
(CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method
produces stable and prominent classifcation accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1,
CD2, CD3, and CD4, respectively).