COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm

• Published in: BioMed research international Vol. 2021; no. 1; p. 2295920
• Main Authors: Farki, Ali, Salekshahrezaee, Zahra, Tofigh, Arash Mohammadi, Ghanavati, Reza, Arandian, Behdad, Chapnevis, Amirahmad
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Computer-aided medical diagnosis; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; Databases, Factual; Datasets; Diagnosis; Diagnosis, Computer-Assisted - methods; Epidemics; Fuzzy algorithms; Fuzzy logic; Fuzzy systems; Humans; Image Enhancement; Machine Learning; Mathematical optimization; Methods; Neural networks; Neural Networks, Computer; Optimization; Radiography - methods; Sensitivity and Specificity; Viral diseases; X-Rays; Iran
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2021/2295920

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others.