Abstract
Over 500 million people have fallen prey to the coronavirus (COVID-19) epidemic that is sweeping the world. The traditional method for detecting it is pathogenic laboratory testing, but it has a high risk of false negatives, forcing the development of additional diagnostic approaches to combat the disease. X-ray imaging is a straightforward and patient-friendly operation that may be performed in almost any healthcare facility. The aim of the report is to use transfer learning models to build a feasible mechanism for determining COVID-19 pneumonia automatically utilizing chest X-ray images while enhancing detection accuracy. On three publicly available datasets, we ran several experiments. The recommended mechanism is intended to provide multi-class classification diagnostics (COVID-19 pneumonia vs. Non COVID-19 pneumonia vs. Normal). In this study, 5 selected best transfer learning methods out of 9 alternative models were tested in various scenarios with varied dataset splitting and amalgamation. Based on their performance with the Merged dataset, an ensemble model was developed using top three models. Our proposed ensemble model had classification accuracy, precision, recall, and f1-score of 99.62%, 1, 0.99, and 1.00 for multi-class cases, respectively. It detected 99.12% of COVID-19 pneumonia accurately. This recommended system can considerably improve COVID-19 diagnosis time and efficiency.
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Seema, S.J., Ahmed, M.U. (2023). A Reliable and Efficient Transfer Learning Approach for Identifying COVID-19 Pneumonia from Chest X-ray. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_11
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