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

Year 2023, Volume: 5 Issue: 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

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.

Keywords

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

Supporting Institution

Inonu University scientific research projects coordination unit

Project Number

TOA-2020-2204

Thanks

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

References

• 1. Guo H, Zhou Y, Liu X, Tan J. The impact of the COVID-19 epidemic on the utilization of emergency dental services. J Dental Sci. 2020;15:564-7.
• 2. Akbulut S, Yağın FH, Çolak C. Prediction of COVID-19 based on genomic biomarkers of metagenomic next-generation sequencing (mNGS) Data using artificial intelligence technology. Erciyes Med J. 2022;44:544–8.
• 3. Cansel N, Karaca Y, Yağin FH. Evaluation of Coronavirus Phobia and Depression in Patients With Cardiovascular Disease. Kahramanmaraş KSU Medical Journal.17:163-71.
• 4. Yaylaci S, Dheir H, Şenocak D, et al. The effects of favipiravir on hematological parameters of covıd-19 patients. Revista Associação Méd Brasileira. 2020;66:65-70.
• 5. Kassania SH, Kassanib PH, Wesolowskic MJ, et al. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Biocybern Biomed Eng. 2021;41:867-79.
• 6. Huang P, Liu T, Huang L, et al. Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology. 2020;295:22-3.
• 7. Perçin İ, Yağin H, Arslan AK, Çolak C, editors. An Interactive Web Tool for Classification Problems Based on Machine Learning Algorithms Using Java Programming Language: Data Classification Software. 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); 2019: IEEE.
• 8. Aggarwal P, Mishra NK, Fatimah B, et al. COVID-19 image classification using deep learning: Advances, challenges and opportunities. Comput Bio Med. 2022:144:105350.
• 9. Madaan V, Roy A, Gupta C, et al. XCOV Net: chest X-ray image classification for COVID-19 early detection using convolutional neural networks. New Gener Comput. 2021;39:583-97.
• 10. Reshi AA, Rustam F, Mehmood A, et al. An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity. 2021;1-12.
• 11. Al-Zubaidi EA, Mijwil MM. Medical image classification for coronavirus disease (COVID-19) using convolutional neural Networks. Iraqi Jo Sci. 2021:2740-7.
• 12. Perçın İ, Yağin FH, Güldoğan E, et al. ARM: an interactive web software for association rules mining and an application in medicine. 2019 International Artificial Intelligence and Data Processing Symposium (IDAP); 2019: IEEE.
• 13. Yağin FH, Yağin B, Arslan AK, Çolak C. Comparison of performances of associative classification methods for cervical cancer prediction: observational study. Turk Clinics J Biostatistics. 2021;13.
• 14. Yilmaz R, Yağin FH. Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical Records. 2022;4:1-6.
• 15. Yousri D, Abd Elaziz M, Abualigah L, et al. COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions. Applied Soft Computing. 2021;101:107052.
• 16. Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020;43:635-40.
• 17. Alqudah AM. Augmented COVID-19 X-ray images dataset. Mendeley Data, v4. 2020.
• 18. Yilmaz R, Yağin FH. A Comparative Study for the Prediction of Heart Attack Risk and Associated Factors Using MLP and RBF Neural Networks. J Cognitive Systems. 2021;6:51-4.
• 19. Theckedath D, Sedamkar R. Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Sci. 2020;1:1-7.
• 20. Tammina S. Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int J Scientific Research Publications (IJSRP). 2019;9:143-50.
• 21. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014.
• 22. Srivastava S, Kumar P, Chaudhry V, Singh A. Detection of ovarian cyst in ultrasound images using fine-tuned VGG-16 deep learning network. SN Computer Science. 2020;1:1-8.
• 23. Yasar S, Yagin FH, Arslan AK, et al. Interactive web-based software for evaluating diagnostic tests and roc curve analyses in health sciences. Annals Med Research. 2021;28:2012-8.
• 24. Paksoy N, Yağin FH. Artificial intelligence-based colon cancer prediction by identifying genomic biomarkers. Med Records. 2022;4:196-202.
• 25. Yağin FH, Güldoğan E, Ucuzal H, Çolak C. A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images. Konuralp Med J. 2021;13:438-45.
• 26. Wang D, Mo J, Zhou G,et al. An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PloS One. 2020;15:e0242535.
• 27. Rasheed J, Hameed AA, Djeddi C, et al. A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci. 2021;13:103-17.