TY  - JOUR
TI  - Development of Artificial Intelligence Based Clinical Decision Support System on Medical Images for the Classification of COVID-19
AB  - 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 (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.
AU  - ÇOLAK, Cemil
AU  - ARSLAN, Ahmet
AU  - Eker, Ayse Gul
AU  - Köse, Adem
AU  - Yıldırım, Ismail Okan
AU  - GÜLDOĞAN, Emek
AU  - KAYACAN, MEHMET CENGIZ
DO  - 10.37990/medr.1130194
PY  - 2023
JO  - Medical records-international medical journal (Online)
VL  - 5
IS  - 1
SN  - 2687-4555
SP  - 20
EP  - 23
DB  - TRDizin
UR  - http://search/yayin/detay/1196634
ER  -