Development of Artificial Intelligence Based Clinical Decision Support System on Medical Images for the Classification of COVID-19

Authors : Cemil ÇOLAK, Ahmet Kadir ARSLAN, Hasan UCUZAL, Adem KÖSE, İsmail Okan YILDIRIM, Emek GÜLDOĞAN, Mehmet Cengiz ÇOLAK

Pages : 20-3

Doi:10.37990/medr.1130194

View : 15 | Download : 5

Publication Date : 2023-01-15

Article Type : Research Paper

Abstract :Aim: The first imaging method to play an vital role in the diagnosis of COVID-19 illness is the chest X-ray. Because of the abundance of large-scale annotated picture datasets, convolutional neural networks insert ignore into journalissuearticles values(CNNs); have shown considerable performance in image recognition/classification. The current study aims to construct a successful deep learning model that can distinguish COVID-19 from healthy controls using chest X-ray images. Material and Methods: The dataset in the study consists of subjects with 912 negative and 912 positive PCR results. A prediction model was built using VGG-16 with transfer learning for classifying COVID-19 chest X-ray images. The data set was split at random into 80% training and 20% testing groups. Results: The accuracy, F1 score, sensitivity, specificity, positive and negative values from the model that can successfully distinguish COVID-19 from healthy controls are 97.3%, 97.3%, 97.8%, 96.7%, 96.7%, and 97.8% regarding the testing dataset, respectively. Conclusion: The suggested technique might greatly improve on current radiology-based methodologies and serve as a beneficial tool for clinicians/radiologists in diagnosing and following up on COVID-19 patients.
Keywords : COVID 19, image processing, convolutional neural networks, classification