Arijit Dey: Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, 700064, India. ORCID
Soham Chattopadhyay: Department of Electrical Engineering, Jadavpur University, 188, Raja S. C. Mallick Road, Kolkata, West Bengal, 700032, India. ORCID
Pawan Kumar Singh: Department of Information Technology, Jadavpur University, Jadavpur University Second Campus, Plot No. 8, Salt Lake Bypass, LB Block, Sector III, Salt Lake City, Kolkata, West Bengal, 700106, India. ORCID
Ali Ahmadian: Institute of IR 4.0, The National University of Malaysia, 43600, Bangi, Malaysia. ahmadian.hosseini@gmail.com. ORCID
Massimiliano Ferrara: Department of Management and Technology, ICRIOS - The Invernizzi Centre for Research in Innovation, Organization, Strategy and Entrepreneurship, Bocconi University, Via Sarfatti, 25, Milan, MI, 20136, Italy. massimiliano.ferrara@unirc.it. ORCID
Norazak Senu: Institute for Mathematical Research, Universiti Putra Malaysia (UPM), 43400, Selangor, Malaysia. ORCID
Ram Sarkar: Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata, West Bengal, 700032, India. ORCID
COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.
References
IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2775-2780
[PMID: 33705321]
Stud Health Technol Inform. 2020 Jun 26;272:13-16
[PMID: 32604588]
Internet Things (Amst). 2021 Jun;14:100377
[PMID: 38620521]
