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

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 22
• Main Authors: Mohammed, Mazin Abed, Al-Khateeb, Belal, Yousif, Mohammed, Mostafa, Salama A., Kadry, Seifedine, Abdulkareem, Karrar Hameed, Garcia-Zapirain, Begonya
• Format: Journal Article
• Language: English
• Published: United States Hindawi 13.08.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Animals; Artificial intelligence; Computed tomography; Coronaviruses; COVID-19; COVID-19 - diagnosis; COVID-19 Testing; Crows; CT imaging; Datasets; Decision making; Deep Learning; Diagnosis; Epidemics; Fitness; Health care; Hospitals; Humans; Image contrast; Iraq; Lungs; Machine learning; Mathematical optimization; Medical imaging; Optimization; Optimization algorithms; Pandemics; Performance evaluation; Seminars; Iraq; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/1307944

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.