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

Yıl 2023, Cilt: 5 Sayı: 1, 20 - 3, 15.01.2023

Cemil Çolak , Ahmet Kadir Arslan , Hasan Ucuzal , Adem Köse , İsmail Okan Yıldırım , Emek Güldoğan , Mehmet Cengiz Çolak

https://doi.org/10.37990/medr.1130194

Öz

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.

Anahtar Kelimeler

COVID-19, image processing, convolutional neural networks, classification.

Destekleyen Kurum

Inonu University scientific research projects coordination unit

Proje Numarası

TOA-2020-2204

Teşekkür

We would like to acknowledge the Inonu University scientific research projects coordination unit for their support with the TOA-2020-2204 project.

Kaynakça

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