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Harris Hawks optimisation with Simulated Annealing as a deep feature selection method for screening of COVID-19
Rajarshi Bandyopadhyay1, Arpan Basu1, Erik Cuevas2
1Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.
Insights
This study introduces an automated method using Convolutional Neural Networks (CNNs) and Harris Hawks Optimization (HHO) for detecting Coronavirus disease 2019 (COVID-19) in CT scans. The enhanced HHO algorithm significantly improves detection accuracy and reduces feature selection.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Biology
Background:
- Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, can lead to severe respiratory illness and death.
- Radiological image analysis offers potential for automated COVID-19 screening.
- Accurate and efficient detection methods are crucial for patient management and public health.
Purpose of the Study:
- To propose a two-stage automated pipeline for COVID-19 detection in CT scan images.
- To enhance feature selection using an optimized meta-heuristic algorithm.
- To improve the accuracy and efficiency of COVID-19 diagnosis from radiological data.
Main Methods:
- A two-stage pipeline involving feature extraction using a DenseNet-based CNN and feature selection.
- Implementation of the Harris Hawks Optimization (HHO) algorithm, enhanced with Simulated Annealing (SA) and Chaotic Initialization, for feature selection.
- Evaluation of the proposed method on the SARS-COV-2 CT-Scan dataset comprising 2482 CT scans.
Main Results:
- The proposed method achieved an accuracy of 98.85% with the enhanced HHO algorithm (including SA and Chaotic Initialization).
- The inclusion of SA and Chaotic Initialization improved accuracy from 98.42% to 98.85%.
- The algorithm reduced the number of selected features by approximately 75%, outperforming many other feature selection algorithms.
Conclusions:
- The developed automated pipeline demonstrates high accuracy and efficiency in detecting COVID-19 from CT scans.
- The enhanced HHO algorithm with SA and Chaotic Initialization is effective for feature selection in medical image analysis.
- This approach offers a promising tool for augmenting the screening and diagnosis of COVID-19.
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.
