Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Abstract

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of which COVID-19 diagnostic model should be selected and which decision-makers of healthcare organizations should consider performance criteria. Because of this, a selection scheme is necessary to address all the above issues. This study proposes an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis. The crow swarm optimization is employed to find an optimal set of coefficients using a designed fitness function for evaluating the performance of the deep learning models. The crow swarm optimization is modified to obtain a good selected coefficient distribution by considering the best average fitness. We have utilized two datasets: the first dataset includes 746 computed tomography images, 349 of them are of confirmed COVID-19 cases and the other 397 are of healthy individuals, and the second dataset are composed of unimproved computed tomography images of the lung for 632 positive cases of COVID-19 with 15 trained and pretrained deep learning models with nine evaluation metrics are used to evaluate the developed methodology. Among the pretrained CNN and deep models using the first dataset, ResNet50 has an accuracy of 91.46% and a F1-score of 90.49%. For the first dataset, the ResNet50 algorithm is the optimal deep learning model selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5715.988 for COVID-19 computed tomography lung images case considered differential advancement. In contrast, the VGG16 algorithm is the optimal deep learning model is selected as the ideal identification approach for COVID-19 with the closeness overall fitness value of 5758.791 for the second dataset. Overall, InceptionV3 had the lowest performance for both datasets. The proposed evaluation methodology is a helpful tool to assist healthcare managers in selecting and evaluating the optimal COVID-19 diagnosis models based on deep learning.

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans

“…CT images provide a more detailed view of the lungs, soft tissue, and blood vessels [ 24 ]. Mohammed et al [ 25 ] proposed an integrated method for selecting the optimal deep learning model based on a novel crow swarm optimization algorithm for COVID-19 diagnosis using CT images. Saeed et al [ 26 ] proposed a method based on complex fuzzy hyper-soft sets, which is a formulation of complex fuzzy (CF) and hyper-soft sets for the classification of COVID-19 and non-COVID-19.…”

COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold

“…For example, CNN based on Sugeno fuzzy integral was devoted to detecting GGO in COVID-19 [24] . Anam-net improved by a lightweight CNN was applied to segment GGO [25] , [26] . In particular, detecting GGO can also be utilized feature selection based on particle swarm optimization algorithm, and ant colony algorithm [27] .…”

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

“…Basic performance indicators including the R , RMSE , MAPE , MAE , NS , and a20-index were taken into account while evaluating the effectiveness of the CF and ANN models in order to determine the models’ reliability [ 64 , 65 , 66 , 67 , 68 ]. It is clear that COVID-19 has affected many areas of our life, including our environment, activities, and other factors, and may require intelligent solutions using AI techniques, medical images, and clinical data to control the pandemic [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. Formulas (11) to (16) below give the equations for the performance indices listed above.…”