ARTIFICIAL INTELLIGENCE-ASSISTED PREDICTION OF COVID-19 STATUS BASED ON THORAX CT SCANS USING A PROPOSED META-LEARNING STRATEGY
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Carbone Editore
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Background: Radiological techniques integrated with artificial intelligence (AI) are a promising diagnostic tool for the rapidly increasing number of COVID-19 cases today. In this study, we intended to construct an artificial intelligence-assisted prediction of COVID-19 status based on thorax computed tomography (CT) scans using a proposed meta-learning strategy. Methods: A public dataset including 1252 positive and 1230 negative thorax CT scans of SARS-CoV-2 was used in the current study. The CT images for COVID-19 status were analyzed by 26 transfer learning (TL) models. The stacking ensemble learning was used to obtain more consistent and high-performance prediction results by combining the prediction results of 26 TL models with an Results: Mobile had the best prediction with an accuracy of 0.946 (95% CI: 0.93-0.962) among the TL models. The Meta-learning model yielded the best classification accuracy of 0.993 (0.98-1), which outperformed MobileNet, the most successful architecture Conclusions: The proposed meta-model that can distinguish CT images between COVID-19 positive and abnormal/normal conditions due to other etiology of COVID-19 negative may be beneficial in such pandemics. The AI application in this study can be used in mobile, desktop, and web-based platforms to have facilitating and complementary effects on classical reporting and the current workload in radiology departments.
Açıklama
Anahtar Kelimeler
Computed tomography, COVID-19, transfer learning models, meta-learning, XGBoost
Kaynak
Acta Medica Mediterranea
WoS Q Değeri
Q4
Scopus Q Değeri
N/A
Cilt
38
Sayı
3
