Examining The Effect of Pre-processed Covid-19 Images On Classification Performance Using Deep Learning Method

Abstract

In recent years, researchers have been using different artificial intelligence models to process x-ray images and make a determination about the patient's condition. Pre-processing is applied to medical images by many researchers. In this way, researchers know that the results they will obtain will be better and that their study results will be more accepted in the literature. As with all other medical images, pre-processing of Covid-19 images is generally done to obtain better classification results. In this study, some pre-processing was done with Covid-19 images. Experimental studies were performed using the ResNet18 deep learning model. According to experimental studies carried out on non pre-processed images, an average accuracy of 0.85206% was obtained in the test processes, while an accuracy rate of 0.93086% was obtained in the test processes obtained from pre-processed images. It was observed that better results were obtained by processing pre-processed images with the same model.

Keywords

• Covid-19
• deep learning
• image pre-processing

References

1. [1] Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers R. M. (2017). Chestx Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2097-2106.
2. [2] Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, Tan W. (2020). Genomic Characterisation and Epidemiology of 2019 Novel Coronavirus: Ġmplications for Virus Origins and Receptor Binding, The Lancet, 395(10224), 565-574.
3. [3] Horry M J, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N, (2020). COVID-19 detection through transfer learning using multimodal imaging data, IEEE Access, 8, 149808-149824
4. [4] Ucar F, Korkmaz D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images, Med Hypotheses, 140:109761.
5. [5] Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z, Yan F, Ding Z, Yang Q, Song B, Shi F, Yuan H, Wei Y, Cao X, Gao Y, Wu D, Wang Q, Shen D, (2020). Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia, IEEE Transactions on Medical Imaging, 39 (8), 2595-2605.
6. [6] Jaiswal A, Gianchandani N, Singh D, Kumar V, Kaur M, (2021). Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, Journal of Biomolecular Structure and Dynamics, 39 (15), 5682-5689.
7. [7] Wang L, Lin Z Q, Wong A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images, Scientific Reports, 10(1), 1-12.
8. [8] Albahli S, Ayub N, Shiraz M. (2021).Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet, Applied Soft Computing, 110.

9. [9] Goel T, Murugan R, Mirjalili S, Chakrabartty D K. (2021). Automatic screening of covid-19 using an optimized generative adversarial network, Cognitive Computation, 1-16.
10. [10] Liang X, Zhang Y, Wang J, Ye Q, Liu Y, Tong J. (2021). Diagnosis of COVID-19 pneumonia based on graph convolutional network, Frontiers in Medicine, 7, 612962.
11. [11] Alshazly H, Linse C, Barth E, Martinetz T. (2021). Explainable COVID-19 detection using chest CT scans and deep learning, Sensors, 21 (2), 455.
12. [12] Chaudhary P K, Pachori R B. (2021). FBSED based automatic diagnosis of COVID-19 using X-ray and CT images, Computers in Biology and Medicine, 134, 104454.
13. [13] Avuçlu E, (2022). A novel method using Covid-19 dataset and machine learning algorithms FOR THE MOST ACCURATE DIAGNOSIS that can be obtained in medical diagnosis, Biomedical Signal Processing and Control, 77, 103836, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2022.103836.
14. [14] Avuçlu E, (2022). COVID-19 detection using X-ray images and statistical measurements, Measurement, Volume 201, 111702, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2022.111702.
15. [15] Elen A. (2022). Covid-19 detection from radiographs by feature-reinforced ensemble learning, Concurrency Computat Pract Exper, 34( 23):e7179. doi:10.1002/cpe.7179
16. [16] Aydemir, F. (2020). IoT Based Indoor Disinfection Coordinating System Against the New Coronavirus. International Scientific and Vocational Studies Journal, 4(2), 81-85. https://doi.org/10.47897/bilmes.751995
17. [17] Kaiming H, Xiangyu Z, Shaoqing R, Jian S. (2016). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778
18. [18] Kaya V. (2021). Otonom Güvenlik Kontrollerinde Kullanılmak Üzere Derin Öğrenme Tabanlı Silah Tespit Ve Tanıma Sistemi, Doktora Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elektrik Elektronik Mühendisliği Anabilim Dalı Elektrik Elektronik Mühendisliği Teknolojileri Programı.
19. [19] Web site, Covid-19 Image Dataset, https://www.kaggle.com/datasets/pranavraikokte/covid19-image-dataset/code?resource=download, Accessed Date:14.5.2023.