Research Article
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Year 2023, Volume: 12 Issue: 4, 1015 - 1027, 28.12.2023

Talha Burak Alakuş Muhammet Baykara

https://doi.org/10.17798/bitlisfen.1312360

Abstract

References

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  • [19] R. Malhotra, H. Patel, and B. D. Fataniya,” Prediction of COVID-19 disease with chest X-Rays using convolutional neural network,” in Proc of the 3rd Int. Conf. on Inventive Research in Computing Applications, ICIRCA 2021, Coimbatore, India, September 2-4, 2021.
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  • [24] T. Rahman, M. Chowdhury, and A. Khandakar, “COVID-19 Radiography Database,” Kaggle.com, 2021. [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database. [Accessed: Oct. 4, 2023].
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  • [26] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J Big Data, vol. 6, pp. 1-48, July 2019.
  • [27] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc of the 3rd Int. Conf. on Learning Representations, ICLR 2015, San Diego, USA, May 7-9, 2015.
  • [28] R. Mitchell and E. Frank, Accelerating the XGBoost algorithm using GPU computing,” PeerJ Comput Sci., vol. 3, pp. 1-37, July 2017.
  • [29] Y. Shihong, L. Ping, and H. Peiyi, “SVM classification:Its contents and challenges,” Appl Math J Chin Univ., vol. 18, pp. 332-342, September 2003.
  • [30] K. He et al., “Deep residual learning for image recognition,” in Proc of the Conf. on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, USA, June 27-30, 2016, pp. 770-778.
  • [31] F. Chollet, “Xception: deep learning with depthwise separable convolutions”, in Proc of the Conf. on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, USA, July 21-26, 2017, pp. 1800-1807.
  • [32] G. Huang et al., “Densely connected convolutional networks,” in Proc of the Conf. on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, USA, July 21-26, 2017, pp. 2261-2269.
  • [33] T. Y. Chen, FC. Kuo, and R. Merkel, “On the statistical properties of the f-measure,” in Proc of the 4th Int. Conf. on Quality Software, QSIC 2004, Washington, USA, September 8-10, 2004, pp. 146-153.
  • [34] G. Caseneuve et al., “Chest X-ray image preprocessing for disease classification,” Procedia Comput Sci., vol. 192, pp. 658-665, October 2021.
  • [35] G. Hussain and Y. Shiren, “Recognition of COVID-19 disease utilizing -ray imaging of the chest using CNN,” in Proc of the Int. Conf. on Computing, Electronics & Communications Engineering, iCCECE 2021, Southend, UK, August 16-17, 2021, pp. 71-76.
  • [36] A. S. Musallam, A. S. Sherif, and M. K. Hussein, “Efficient framework for detecting COVID-19 and pneumonia from chest X-ray using deep convolutional network,” Egypt Inf J., vol. 23, pp. 247-257, July 2022.
  • [37] S. Singht et al., “CNN based Covid-aid: Covid 19 detection using chest X-ray,” in Proc of the 5th Int. Conf. on Computing Methodologies and Communication, ICCMC 2021, Erode, India, April 8-10, 2021, pp. 1791-1797.
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  • [45] D. K. Sharma et al., “Classification of COVID-19 by using supervised optimized machine learning technique,” Mater Today Proc., vol. 56, pp. 2058-2062, November 2021.
  • [46] S. S. Verma, A. Prasad, and A. Kumar, “CovXmlc: high performance COVID-19 detection on X-ray images using multi-model classification,” Biomed Signal Process Control, vol. 71, pp. 1-7, January 2022.
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  • [48] H. Nasiri and S. Hasani, “Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost,” Radiography, vol. 28, pp. 732-738, August 2022.
  • [49] A. Arivoli, D. Golwala, and R. Reddy, “CoviExpert: COVID-19 detection from chest X-ray using CNN,” Measur Sens., vol. 23, pp. 1-8, October 2022.
  • [50] X. Li et al., “A self-supervised feature-standardization-block for cross-domain lung disease classification,” Methods., vol. 202, pp. 70-77, June 2022.
  • [51] S. Kumar et al., “Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19,” Comput Electr Eng., vol. 103, pp. 1-19, October 2022.

Multi Deep Learning Based Approaches for COVID-19 Diagnosis Using Class Resampling on Chest X-ray Images

Year 2023, Volume: 12 Issue: 4, 1015 - 1027, 28.12.2023

Talha Burak Alakuş Muhammet Baykara

https://doi.org/10.17798/bitlisfen.1312360

Abstract

Nowadays, current medical imaging techniques provide means of diagnosing disorders like the recent COVID-19 and pneumonia due to technological advancements in medicine. However, the lack of sufficient medical experts, particularly amidst the breakout of the epidemic, poses severe challenges in early diagnoses and treatments, resulting in complications and unexpected fatalities. In this study, a convolutional neural network (CNN) model, VGG16 + XGBoost and VGG16 + SVM hybrid models, were used for three-class image classification on a generated dataset named Dataset-A with 6,432 chest X-ray (CXR) images (containing Normal, Covid-19, and Pneumonia classes). Then, pre-trained ResNet50, Xception, and DenseNet201 models were employed for binary classification on Dataset-B with 7,000 images (consisting of Normal and Covid-19). The suggested CNN model achieved a test accuracy of 98.91 %. Then the hybrid models (VGG16 + XGBoost and VGG16 + SVM) gained accuracies of 98.44 % and 95.60 %, respectively. The fine-tuned ResNet50, Xception, and DenseNet201 models achieved accuracies of 98.90 %, 99.14 %, and 99.00 %, respectively. Finally, the models were further evaluated and tested, yielding impressive results. These outcomes demonstrate that the models can aid radiologists with robust tools for early lungs related disease diagnoses and treatment.

Keywords

Artificial intelligence Deep learning Medical imaging Transfer learning.

References

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  • [2] M. A. Alah, S. Abdeen, and V. Kehyayan, “The first few cases and fatalities of corona virus disease 2019 (COVID-19) in the Eastern Mediterranean Region of the World Health Organization: a rapid review,” J Infect Public Health, vol. 13, pp. 1367-1372, October 2020.
  • [3] J. S. Mackenzie, and D. W. Smith, “COVID-19: a novel zoonotic disease caused by a coronavirus from China: what we know and what we don't,” Microbiol Aust., pp. 1-14, March 2020.
  • [4] A. R. Rahmani et al., “Sampling and detection of corona viruses in air: a mini review,” Sci Total Environ., vol. 740, pp. 1-7, October 2020.
  • [5] J. She, L. Liu, and W. Liu, “COVID-19 epidemic: Disease characteristics in children,” J Med Virol., vol. 92, pp. 747-754, April 2020.
  • [6] L. Baroiu et al., “COVID-19 impact on the liver,” World J Clin Cases., vol. 9, pp. 3814-3825, June 2021.
  • [7] P. Sirohiya et al., “Airway management, procedural data, and in-hospital mortality records of patients undergoing surgery for mucormycosis associated with coronavirus disease (COVID-19),” J Mycol Med., vol. 32, pp. 1-6, November 2020.
  • [8] Y. Rolland et al., “Coronavirus disease-2019 in older people with cognitive impairment,” Clin Geriatr Med., vol. 38, pp. 501-507, August 2022.
  • [9] M. Sachdeva et al., “Cutaneous manifestations of COVID-19: report of three cases and a review of literature,” J Dermatol Sci., vol. 98, pp. 75-81, May 2020.
  • [10] M. Bansal, “Cardiovascular disease and COVID-19,” Diabetes Metab Syndr., vol. 14, pp. 247-250, May 2020. [11] B. P. Goodman et al., “COVID-19 dysautonomia,” Front Neurol., vol. 12, pp. 1-5, April 2021.
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  • [13] A. Barragan-Montero et al., “Artificial intelligence and machine learning for medical imaging: a technology review,” Phys Med., vol. 83, pp. 242-256, March 2021.
  • [14] M. Aljabri and M. AlGhamdi, “A review on the use of deep learning for medical images segmentation,” Neurocomputing, vol. 506, pp. 311-335, September 2022.
  • [15] P. Asha et al. “Artificial intelligence in medical Imaging: An analysis of innovative technique and its future promise,” Mater Today Proc., vol. 56, pp. 2236-2239, December 2021.
  • [16] A. Singhal et al., “Study of deep learning techniques for medical image analysis: a review,” Mater Today Proc., vol. 56, pp. 209-214, January 2022.
  • [17] M. E. Sahin, “Deep learning-based approach for detecting COVID-19 in chest X-rays,” Biomed Signal Process Control., vol. 78, pp. 1-10, September 2022.
  • [18] S. Hassantabar, M. Ahmadi, and A. Sharifi, “Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches,” Chaos Solitons Fractals., vol. 140, pp. 1-11, November 2020.
  • [19] R. Malhotra, H. Patel, and B. D. Fataniya,” Prediction of COVID-19 disease with chest X-Rays using convolutional neural network,” in Proc of the 3rd Int. Conf. on Inventive Research in Computing Applications, ICIRCA 2021, Coimbatore, India, September 2-4, 2021.
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  • [21] A. Banerjee et al., “COVID-19 chest X-ray detection through blending ensemble of CNN snapshots,” Biomed Signal Process Control., vol. 78, pp. 1-9, September 2022.
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  • [26] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J Big Data, vol. 6, pp. 1-48, July 2019.
  • [27] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc of the 3rd Int. Conf. on Learning Representations, ICLR 2015, San Diego, USA, May 7-9, 2015.
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  • [29] Y. Shihong, L. Ping, and H. Peiyi, “SVM classification:Its contents and challenges,” Appl Math J Chin Univ., vol. 18, pp. 332-342, September 2003.
  • [30] K. He et al., “Deep residual learning for image recognition,” in Proc of the Conf. on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, USA, June 27-30, 2016, pp. 770-778.
  • [31] F. Chollet, “Xception: deep learning with depthwise separable convolutions”, in Proc of the Conf. on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, USA, July 21-26, 2017, pp. 1800-1807.
  • [32] G. Huang et al., “Densely connected convolutional networks,” in Proc of the Conf. on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, USA, July 21-26, 2017, pp. 2261-2269.
  • [33] T. Y. Chen, FC. Kuo, and R. Merkel, “On the statistical properties of the f-measure,” in Proc of the 4th Int. Conf. on Quality Software, QSIC 2004, Washington, USA, September 8-10, 2004, pp. 146-153.
  • [34] G. Caseneuve et al., “Chest X-ray image preprocessing for disease classification,” Procedia Comput Sci., vol. 192, pp. 658-665, October 2021.
  • [35] G. Hussain and Y. Shiren, “Recognition of COVID-19 disease utilizing -ray imaging of the chest using CNN,” in Proc of the Int. Conf. on Computing, Electronics & Communications Engineering, iCCECE 2021, Southend, UK, August 16-17, 2021, pp. 71-76.
  • [36] A. S. Musallam, A. S. Sherif, and M. K. Hussein, “Efficient framework for detecting COVID-19 and pneumonia from chest X-ray using deep convolutional network,” Egypt Inf J., vol. 23, pp. 247-257, July 2022.
  • [37] S. Singht et al., “CNN based Covid-aid: Covid 19 detection using chest X-ray,” in Proc of the 5th Int. Conf. on Computing Methodologies and Communication, ICCMC 2021, Erode, India, April 8-10, 2021, pp. 1791-1797.
  • [38] G. Gilanie et al., “Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks,” Biomed Signal Process Control, vol. 66, pp. 1-6, April 2021.
  • [39] T. Mahmud, A. Rahman, and S. A. Fattah, “CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization,” Comput Biol Med., vol. 122, pp. 1-10, July 2020.
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  • [42] A. Abbas, M. M. Abdelsamea, and M. M. Gaber, “4S-DT: self-supervised super sample decomposition for transfer learning with application to COVID-19 detection,” IEEE Trans Neural Netw Learn Syst., vol. 32, pp. 2798-2808, July 2021.
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  • [44] M. Toğaçar, “Disease type detection in lung and colon cancer images using the complement approach of inefficient sets,” Comput Biol Med., vol. 137, pp. 1-13, October 2021.
  • [45] D. K. Sharma et al., “Classification of COVID-19 by using supervised optimized machine learning technique,” Mater Today Proc., vol. 56, pp. 2058-2062, November 2021.
  • [46] S. S. Verma, A. Prasad, and A. Kumar, “CovXmlc: high performance COVID-19 detection on X-ray images using multi-model classification,” Biomed Signal Process Control, vol. 71, pp. 1-7, January 2022.
  • [47] B. Prabha et al., “Intelligent predictions of Covid disease based on lung CT images using machine learning strategy,” Mayer Today Proc., vol. 80, pp. 3744-3750, July 2021.
  • [48] H. Nasiri and S. Hasani, “Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost,” Radiography, vol. 28, pp. 732-738, August 2022.
  • [49] A. Arivoli, D. Golwala, and R. Reddy, “CoviExpert: COVID-19 detection from chest X-ray using CNN,” Measur Sens., vol. 23, pp. 1-8, October 2022.
  • [50] X. Li et al., “A self-supervised feature-standardization-block for cross-domain lung disease classification,” Methods., vol. 202, pp. 70-77, June 2022.
  • [51] S. Kumar et al., “Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19,” Comput Electr Eng., vol. 103, pp. 1-19, October 2022.
There are 50 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Araştırma Makalesi
Authors

Talha Burak Alakuş 0000-0003-3136-3341

Muhammet Baykara 0000-0001-5223-1343

Early Pub Date December 25, 2023
Publication Date December 28, 2023
Submission Date June 12, 2023
Acceptance Date October 11, 2023
Published in Issue Year 2023 Volume: 12 Issue: 4

Cite

IEEE T. B. Alakuş and M. Baykara, “Multi Deep Learning Based Approaches for COVID-19 Diagnosis Using Class Resampling on Chest X-ray Images”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 4, pp. 1015–1027, 2023, doi: 10.17798/bitlisfen.1312360.
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Bitlis Eren University
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Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS