Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19 CT-scans

Abstract

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause severe ailments in infected individuals. The more severe cases may lead to death. Automated methods which can detect COVID-19 in radiological images can help in the screening of patients. In this work, a two-stage pipeline composed of feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images is proposed. For feature extraction, a state-of-the-art Convolutional Neural Network (CNN) model based on the DenseNet architecture is utilised. To eliminate the non-informative and redundant features, the meta-heuristic called Harris Hawks optimisation (HHO) algorithm combined with Simulated Annealing (SA) and Chaotic initialisation is employed. The proposed approach is evaluated on the SARS-COV-2 CT-Scan dataset which consists of 2482 CT scans. Without the Chaotic initialisation and the SA, the method gives an accuracy of around 98.42% which further increases to 98.85 % on the inclusion of the two and thus delivers better performance than many state-of-the-art methods and various meta-heuristic based FS algorithms. Also comparison has been drawn with many hybrid variants of meta-heuristic algorithms. Although HHO falls behind a few of the hybrid variants, when Chaotic initialisation and SA are incorporated into it, the proposed algorithm performs better than any other algorithm with which comparison has been drawn. The proposed algorithm decreases the number of features selected by around 75% , which is better than most of the other algorithms.

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

“…They compared their algorithm, which is called HHO-FKNN, with several machine learning algorithms and argued that it has a higher classification and better stability. Moreover, Bandyopadhyay et al [296] used their hybrid algorithm, which combined chaotic HHO with SA, to screen COVID-19 CT scans. 6.10.…”

MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features

“…This model has got an accuracy of 87.02% for three-class classification and 98.08% for two-class classification. Moreover, Rajarshi et al 18 have developed a model which extracts deep features from various CNNs and thereafter the optimal feature subset selection has been done using Harris Hawks optimisation with Simulated Annealing algorithm. The proposed method has been evaluated on SARS-COV-2 CT-Scan dataset and their obtained accuracy was 98.85%.…”

Rules embedded harris hawks optimizer for large-scale optimization problems

“…Both models were applied for the problem of features selection, and their analysis showed great improvements in tackling feature selection challenges. Very recent hybrid studies which integrate HHO with other metaheuristic optimization algorithms were discussed by Abba et al [ 27 ], Ebrahim et al [ 28 ], Bandyopadhyay et al [ 29 ], Suresh et al [ 30 ], and Mossa et al [ 31 ]. In [ 27 ], the hybrid of PSO with HHO for renewable energy load demand forecasting was presented.…”

“…The synergy of sine–cosine with HHO was discussed in [ 28 ] for optimizing the fuel cell−based electric power system. Bandyopadhyay et al [ 29 ] presented the integration of simulated annealing with HHO for deep features selection of COVID-19 from CT-scan images. The hybrid of chaotic multi-verse optimizer with HHO was given in [ 30 ] for the problem of medical diagnosis.…”