An oppositional-Cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis

Abstract

A novel coronavirus (COVID-19) has globally attracted attention as a severe respiratory condition. The epidemic has been first tracked in Wuhan, China, and has progressively been expanded in the entire world. The growing expansion of COVID-19 around the globe has made X-ray images crucial for accelerated diagnostics. Therefore, an effective computerized system must be established as a matter of urgency, to facilitate health care professionals in recognizing X-ray images from COVID-19 patients. In this work, we design a novel artificial intelligent-based automated X-ray image analysis framework based on an ensemble of deep optimized convolutional neural networks (CNNs) in order to distinguish coronavirus patients from non-patients. By developing a modified version of gaining–sharing knowledge (GSK) optimization algorithm using the Opposition-based learning (OBL) and Cauchy mutation operators, the architectures of the deployed deep CNNs are optimized automatically without performing the general trial and error procedures. After obtaining the optimized CNNs, it is also very critical to identify how to decrease the number of ensemble deep CNN classifiers to ensure the classification effectiveness. To this end, a selective ensemble approach is proposed for COVID-19 X-ray based image classification using a deep Q network that combines reinforcement learning (RL) with the optimized CNNs. This approach increases the model performance in particular and therefore decreases the ensemble size of classifiers. The experimental results show that the proposed deep RL optimized ensemble approach has an excellent performance over two popular X-ray image based COVID-19 datasets. Our proposed advanced algorithm can accurately identify the COVID-19 patients from the normal individuals with a significant accuracy of 0.991441, precision of 0.993568, recall (sensitivity) of 0.981445, F-measure of 0.989666 and AUC of 0.990337 for Kaggle dataset as well as an excellent accuracy of 0.987742, precision of 0.984334, recall (sensitivity) of 0.989123, F-measure of 0.984939 and AUC of 0.988466 for Mendely dataset.

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

“…We employ five different metrics namely, the accuracy, precision, recall, F-measure, and area under the curve (AUC) to assess the efficiency of the proposed classification model and other compared benchmarks. These metrics are widely used in medical area and are formulated as follows [ 76 , 77 ]: where TP, FP, TN and FN given in Eqs (17) – (21) refer to True Positive, False Positive, True Negative and False Negative, respectively. Given a test dataset and a deep learning classification Algorithm, TP represents the proportion of positive (i.e., COVID-19) samples that are correctly labeled as COVID-19 by the classification algorithm; FP represents the proportion of negative (i.e., non-COVID-19) samples that are mislabeled as positive; TN is the proportion of negative samples that are correctly labeled as normal and FN is the proportion of positive samples that are mislabeled as negative by the classification algorithm.…”

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

“…Artificial intelligence-based models are used to prevent and mitigate COVID-19 pandemics by screening, identifying viruses, and disease diagnosis, repurposing or repositioning drugs, and predicting and forecasting their future spread. In the area research of medical prediction of COVID-19, intelligent and machine learning-based models grounded on biomarkers can help optimize the screening of patients with severe disease, minimizing mortality and hospitalization, and decreasing care delays [ 44 ].…”

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

“…Rahimzadeh and Attar [ 15 ] combined Xception and ReNet50V2 and obtained classification accuracy of 91.40% on three groups of COVID-19, Pneumonia and Normal. An ensemble learning network DNE-RL optimizing hyper parameters was proposed on two group classification of COVID-19 and Normal, the accuracy was as high as 99.14% [ 16 ]. A computer-aided detection model first combines several pre-trained networks for feature extraction, and then uses a sparse auto-encoder and a feed forward neural network (FFNN) to improve the classification performance.…”