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

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Aim: The first imaging method to play an vital role in the diagnosis of COVID-19 illness is the chest X-ray.

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Temel Bulgular & Veri:Because of the abundance of large-scale annotated picture datasets, convolutional neural networks (CNNs) have shown considerable performance in image recognition/classification.

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Sonuç & Katkı: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.

🔬Tür:Modelleme & Sayısal Simülasyon

📍Kapsam:Genişletilmiş Akademik Örneklem

⚙️Değişkenler:COVID-19 · image processing · convolutional neural networks

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 (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.

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Bu Makaleye Atıf Yap

ÇOLAK, C., ARSLAN, A. K., UCUZAL, H., KÖSE, A., YILDIRIM, İ. O., GÜLDOĞAN, E., ÇOLAK, M. C. (2023). Development of Artificial Intelligence Based Clinical Decision Support System on Medical Images for the Classification of COVID-19. Medical Records, Cilt: 5 - Sayı: 1, 20–3. https://ajindex.com/articles/878030

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Kaynakça (14)

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