ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

Abstract

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected Xray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images.

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.…”

“…Narin et al [ 60 ] used two deeper frameworks for classifying COVID-19 and normal cases based on a publicly available dataset. In a hybrid model named ADOPT [ 61 ], a spotted hyena optimizer, a decision tree model, and deep CNN models are used for identifying COVID-19 pneumonia from X-ray images. 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.…”

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

“…Additionally, many deep learning-based studies have used radiographic images for the detection of COVID-19 and many of them have achieved outstanding classification performance. The following studies can be shown as an example: Al-Waisy et al [ 72 ] achieved accuracy of 99.99%, Dhiman et al [ 79 ] achieved accuracy of 98.54%, Ozturk et al [ 14 ] achieved accuracy of 98.08%, and Ahuja et al [ 74 ] achieved accuracy of 99.4%. The main reason for the success of the mentioned studies is that the most common symptom of COVID-19 disease is lung involvement [ 80 ] and the symptoms can be clearly observed on radiographic lung images [ 81 ].…”

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

“…Thanks to data collected during the earlier phases and to the collaborative effort of many scientists worldwide, some previously conjectured strategies can now be applied in real contexts. As regards AI, the huge number of medical images collected has, for example, enabled medical practitioners to exploit X-ray images (Dhiman et al 2021). These examinations are more economical and expeditious with respect to CTs to diagnose the COVID-19 disease; here, transfer-learning techniques can be exploited.…”