DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images

Abstract

In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease.

A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19)

“…To this end, GA (genetic Algorithm) [ 78 ], PSO (particle swarm optimization) [ 79 ], DE (differential evolution) [ 80 ], GWO (grey wolf optimizer) [ 81 ], MFO (moth-flame optimization) [ 82 ], SSA (salp swarm algorithm) [ 83 ], OSSA (OBL version of SSA), and CSSA (chaotic version of SSA) are considered in comparison with the proposed method. Two powerful evolutionary-based deep-learning models for COVID-19 detection, ADOPT [ 61 ], and DON [ 62 ], are also utilized as the competitive algorithms to show the strength of our proposed model. It should be noted that in the proposed method, the SVM model is used instead of the Softmax model to obtain better results.…”

“…In comparison with eleven different CNN algorithms, ADOPT demonstrated better outcomes in relation to different evaluation metrics such as accuracy, recall, precision, F1-score, and specificity. In another study conducted by Dhiman et al [ 62 ], a model named DON was proposed using emperor penguin optimizer for optimization of the CNN parameters to detect infected patients using X-ray COVID-19 images efficiently. Extensive evaluation findings demonstrate that DON outperforms competitor approaches.…”

Deep learning applied to automatic disease detection using chest X‐rays

“…Adding processes that improve feature selection also improved performance, including correlation‐based feature selection, 60 feature categorisation with decision trees, 61,62 SVMs 63 and even handpicking features 64 …”

Analysis of origin, risk factors influencing COVID-19 cases in India and its prediction using ensemble learning

“…WHO and other medical organizations give the following guidelines and instructions to prevent the spread of COVID-19 infection (Dhiman et al 2021 ): Maintain a strategic distance from individuals with severe CoV illness, especially during communication. People should wash their hands often, as much as they come into touch with persons who are sick with the contagious virus and the locations wherever they reside.…”