Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm

Abstract

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the COVID-Xray-5k and COVIDetectioNet datasets, and the results are verified by comparing it with the classic DCNN, Genetic Algorithm optimized ELM (GA-ELM), Cuckoo Search optimized ELM (CS-ELM), and Whale Optimization Algorithm optimized ELM (WOA-ELM). The proposed approach outperforms other comparative benchmarks with 98.25 % and 99.11 % as ultimate accuracy on the COVID-Xray-5k and COVIDetectioNet datasets, respectively, and it led relative error to reduce as the amount of 1.75 % and 1.01 % as compared to a convolutional CNN. More importantly, the time needed for training deep ChOA-ELM is only 0.9474 milliseconds, and the overall testing time for 3100 images is 2.937 s.

Keywords: COVID-19; Chest X-ray images; Chimp optimization algorithm; Deep convolutional neural networks; Real-time.

Fig. 1

The design of LeNet-5 DCNN.

Fig. 2

Single-hidden layer neural network.

Fig. 3

The chaotic maps used in ChOA.

Fig. 4

Some samples of a) Pneumonia, b) COVID-19, and c) normal cases from the utilized datasets.

Fig. 5

the Conventional vs. Proposed Design.

Fig. 6

A general block diagram of the CELM-ChOA model.

Fig. 7

The Pseudo-code for DCELM-ChOA model.

Fig. 8

The classification accuracy for different chaotic maps.

Fig. 9

the EPG for COVID-Xray-5k dataset.

Fig. 10

the EPG for COVIDetectioNet dataset.

Fig. 11

The Confusion Matrix for COVID-Xray-5k dataset.

Fig. 12

The Confusion Matrix for COVIDetectioNet dataset.

Fig. 13

The ROC Curves and Precision-recall Curves for COVID-Xray-5k dataset.

Fig. 14

The ROC Curves and Precision-recall Curves for COVIDetectioNet dataset.

Fig. 15

ROI for Positive COVID-19 Cases Using ACM.

Fig. 16

ROI for Normal Cases Using ACM.