CoviDetNet: A new COVID-19 diagnostic system based on deep features of chest x-ray

Abstract

COVID-19 has emerged as a global pandemic affecting the world, and its adverse effects on society still continue. So far, about 243.57 million people have been diagnosed with COVID-19, of which about 4.94 million have died. In this study, a new model, called COVIDetNet, is proposed for automated COVID-19 detection. A lightweight CNN architecture trained instead of the popular and pretrained convolution neural network (CNN) models such as VGG16, VGG19, AlexNet, ResNet50, ResNet100, and MobileNetV2 from scratch with chest x-ray (CXR) images was designed. A new feature set was created by concatenating the features of all layers of the designed CNN architecture. Then, the most efficient features chosen among the features concatenating with the Relief feature selection algorithm were classified using the support vector machine (SVM) method. The experimental works were carried out on a public COVID-19 CXR database. Experimental results demonstrated 99.24% accuracy, 99.60% specificity, 99.39% sensitivity, 99.04% precision, and an F1 score of 99.21%. Also, in comparison to AlexNet and VGG16 models, the deep feature extraction durations were reduced by approximately 6-fold and 38-fold, respectively. The COVIDetNet model provided a higher accuracy score than state-of-the-art models when compared to multi-class research studies. Overall, the proposed model will be beneficial for specialist medical staff to detect COVID-19 cases, as it provides faster and higher accuracy than existing CXR-based approaches.

Keywords: COVID‐19; Relief; SVM; automatic detection; deep feature extraction with a lightweight CNN.

FIGURE 1

The total numbers of cases and deaths reported by World Health Organization from different countries are shown in the chart. (The columns and line show the total number of cases and total deaths affected by COVID‐19 from January 3, 2020, to November 12, 2021, respectively.)

FIGURE 2

Structure of the proposed COVIDetNet model

FIGURE 3

Chest x‐ray image samples in the dataset

FIGURE 4

The architecture of the proposed convolution neural network

FIGURE 5

Graphics of loss and accuracy values for validation and training

FIGURE 6

Confusion matrix of the models with the quickest feature extraction times

FIGURE 7

Confusion matrix and receiver operating characteristic curves for all features

FIGURE 8

Confusion matrix and receiver operating characteristic curves for 25 Relief features