Decision and Feature Level Fusion of Deep Features Extracted from Public COVID-19 Data-sets

TL;DR

This study proposes a CNN-based computer-aided diagnosis system that fuses deep features at the decision and feature levels to improve COVID-19 detection accuracy from chest X-ray images, outperforming existing methods.

Contribution

It introduces a novel fusion approach combining feature and decision level techniques for COVID-19 detection using deep CNN features.

Findings

High accuracy in COVID-19 detection from X-ray images.

Fusion approach outperforms individual classifiers and existing studies.

Validated with Class Activation Mapping for interpretability.

Abstract

The Coronavirus (COVID-19), which is an infectious pulmonary disorder, has affected millions of people and has been declared as a global pandemic by the WHO. Due to highly contagious nature of COVID-19 and its high possibility of causing severe conditions in the patients, the development of rapid and accurate diagnostic tools have gained importance. The real-time reverse transcription-polymerize chain reaction (RT-PCR) is used to detect the presence of Coronavirus RNA by using the mucus and saliva mixture samples. But, RT-PCR suffers from having low-sensitivity especially in the early stage. Therefore, the usage of chest radiography has been increasing in the early diagnosis of COVID-19 due to its fast imaging speed, significantly low cost and low dosage exposure of radiation. In our study, a computer-aided diagnosis system for X-ray images based on convolutional neural networks (CNNs), which can be used by radiologists as a supporting tool in COVID-19 detection, has been proposed. Deep feature sets extracted by using CNNs were concatenated for feature level fusion and fed to multiple classifiers in terms of decision level fusion idea with the aim of discriminating COVID-19, pneumonia and no-finding classes. In the decision level fusion idea, a majority voting scheme was applied to the resultant decisions of classifiers. The obtained accuracy values and confusion matrix based evaluation criteria were presented for three progressively created data-sets. The aspects of the proposed method that are superior to existing COVID-19 detection studies have been discussed and the fusion performance of proposed approach was validated visually by using Class Activation Mapping technique. The experimental results show that the proposed approach has attained high COVID-19 detection performance that was proven by its comparable accuracy and superior precision/recall values with the existing studies.