Fusion of Handcrafted Features and Deep Features to Detect COVID-19

Koushik Gunda, Soumendu Chakraborty, Dubravko Culibrk

Abstract: The COVID-19 pandemic’s rapid growth has made it crucial to develop reliable and efficient diagnostic methods. In this study, we incorporate deep features and handcrafted features to provide a unique method for COVID-19 identification using chest X-rays. In order to extract high-level features from the chest X-ray pictures, we first use a convolutional neural network (CNN) that has already been trained to take advantage of deep learning. The discriminative information regarding COVID-19 infection is captured by the obtained deep features. In addition to the deep features, we also use manually created features that are meant to capture the unique features of COVID-19 in chest X-rays. Based on earlier study findings and domain understanding, these characteristics were manually constructed. They consist of statistical measures, shape-based characteristics, and texture descriptors. Comparing the performance of the classification with the standalone applications of convolutional and handcrafted features, we find that combining the features in our innovative framework enhances performance.

External IDs:dblp:conf/cvip/GundaCC23