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COVID-19 Tespitinde Öğrenim Aktarımına CLAHE Tabanlı Geliştirme

Year 2022, Volume: 8 Issue: 2, 406 - 416, 01.09.2022

Abstract

Derin öğrenme kullanılarak göğüs röntgeni görüntülerinin sınıflandırılması için öznitelik öğrenme ve gelişmiş ön işleme aşamaları üzerindeki geliştirmelerle COVID-19 hastalığının erken teşhisi mümkün hale getirmiştir. Bunun yanında, Derin Öğrenme popülaritesi nedeniyle birçok araştırmacı tarafından denenerek yüksek performanslı modeller ortaya sürülmüştür. Bu çalışmada göğüs röntgen filmlerine, AlexNet, MobileNet, VGG16 ve DarkNet19 gibi popüler derin öğrenme mimarilerindeki transfer öğrenme yaklaşımıyla sınıflandırmadan önce Kontrast Sınırlı Adaptif Histogram Eşitleme (CLAHE) kullanılarak ön işlem uygulanmıştır. Makalenin orijinalliği, göğüs röntgeni görüntülerini doğrudan ham veri ile eğitmek yerine önce hava yollarının ve patolojilerin daha belirgin yansımalarını elde edilerek gerçekleştirilmesidir. En başarılı CLAHE parametreleri çeşitli aralıklardaki deneyler sonucunda belirlenmiştir. Önerilen yaklaşımın diğer üstün katkısı, modelin eğitiminde ve testinde 3615 COVID-19'lu ve 3500 sağlıklı göğüs röntgeninden oluşan büyük ölçekli bir veri seti kullanılmasıdır. CLAHE tabanlı öğrenim aktarımı önerisi, en başarılı COVID-19 ve sağlıklı ikili sınıflandırma başarımına %95,878 doğruluk oranıyla VGG16 modeli üzerinde 56 disk değeri ve 0.2 klip limiti CLAHE parametrelerini kullanarak ulaşmıştır.

References

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  • J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-fei, “ImageNet : a Large-Scale Hierarchical Image Database,” in Proc. of 2009 IEEE Conference of Computer Vision Pattern Recognition., 20-25 June 2009, Miami, Florida, [Online]. Available: IEEE Xplore, https://doi.org/10.1109/CVPR.2009.5206848. [Accessed: 8 May 2022].
  • A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arxiv.org, Sep. 4, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556. [Accessed: May 8, 2022].
  • S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding”, arxiv.org, Oct. 1, 2015. [Online]. Available: https://arxiv.org/abs/1510.00149. [Accessed: May 8, 2022].
  • A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications”, arxiv.org, Apr. 17, 2017. [Online]. Available: https://arxiv.org/abs/1704.04861. [Accessed: May 8, 2022].
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  • R.A. Hummel, “Histogram modification techniques,” Computer Graphics and Image Processing, vol. 4, no. 3, pp. 209–224, Sept. 1975, doi: 10.1016/0146-664X(75)90009-X.
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CLAHE based Enhancement to Transfer Learning in COVID-19 Detection

Year 2022, Volume: 8 Issue: 2, 406 - 416, 01.09.2022

Abstract

Early diagnosis of COVID-19 disease becomes possible with the enhancements on feature learning and advanced pre-processing stages for classification of chest X-ray images using deep learning. Besides, high-performance models have been developed by many researchers due to the popularity of Deep Learning. In this study, chest X-ray images were pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) before the classification with particular popular transfer learning approaches in deep learning architectures including AlexNet, MobileNet, VGG16, and DarkNet19. The originality of the paper is pre-processing the images using CLAHE to obtain more significant representations of airways and pathologies instead of training with raw chest X-ray images. The best CLAHE parameters were determined considering the results of various trials at a specified range. The other superior contribution of the proposal is using a large-scale dataset, which is comprised of 3500 healthy and 3615 chest x-rays with COVID-19. The CLAHE-based transfer learning proposal achieved an accuracy rate of 95.878% as the most successful binary classification result for COVID-19 and healthy using VGG16 model and CLAHE parameters including disk value of 56, clip-limit of 0.2.

References

  • S. Jaeger et al., “Automatic Tuberculosis Screening Using Chest Radiographs,” IEEE Transactions on Medical Imaging, vol. 33, no. 2, pp. 233–245, 2014, doi: 10.1109/TMI.2013.2284099.
  • A. A. El-solh, C. Hsiao, S. Goodnough, J. Serghani, and B. J. B. Grant, “Predicting active pulmonary Tuberculosis Using an Artificial Neural Network,” Chest, vol. 116, no. 4, pp. 968–973, 1999, doi: 10.1378/chest.116.4.968.
  • M. E. H. Chowdhury et al., “Can AI Help in Screening Viral and COVID-19 Pneumonia?,” IEEE Access, vol. 8, pp. 132665–132676, 2020, doi: 10.1109/ACCESS.2020.3010287.
  • I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,” Physical and Engineering Sciences in Medicine., vol. 43, no. 2, pp. 635–640, 2020, doi: 10.1007/s13246-020-00865-4.
  • G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis. vol.42, pp. 60–88, 2017, doi: 10.1016/j.media.2017.07.005.
  • S. Rajaraman, S. Sornapudi, M. Kohli, and S. Antani, “Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs,”, in Proc. of 41st Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, 23-27 July 2019, Berlin, Germany [Online]. Available: IEEE Xplore, https://doi.org/10.1109/EMBC.2019.8856715. [Accessed: 8 May 2022].
  • R. Hooda, A. Mittal, and S. Sofat, “Lung segmentation in chest radiographs using fully convolutional networks,” Turkish Journal of Electrical Engineering and Computer Science., vol.27, pp.710– 722, 2019, doi: 10.3906/elk-1710-157.
  • P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis,” in Proc. of 7th Int. Conference on Document Analysis and Recognition (Icdar) 2003. 6 Aug. 2003, Edinburgh, UK [Online]. Available: IEEE Xplore, https://doi.org/10.1109/ICDAR.2003.1227801. [Accessed: 8 May 2022].
  • Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, doi: 10.1109/5.726791.
  • Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015, doi:10.1038/nature14539.
  • V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proc. of ICML'10: 27th International Conference on Machine Learning, June 21-24, 2010, Haifa, Israel [Online]. Available: Toronto University, http:// https://www.cs.toronto.edu/~fritz/absps/reluICML.pdf. [Accessed: 8 May 2022].
  • D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber, “Flexible, high performance convolutional neural networks for image classification,” in Proc. of IJCAI'11: the 22th international joint conference on Artificial Intelligence, vol. 2, July 2011, Barcelona Catalonia, Spain, pp. 1237–1242, [Online]. Available: Acm Digital Library, https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-210. [Accessed: 8 May 2022].
  • G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors”, arxiv.org, Jul. 3, 2012. [Online]. Available: https://arxiv.org/abs/1207.0580. [Accessed: May 8, 2022].
  • M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in Proc. of 13th European Conference of Computer Vision – ECCV 2014, 2014, Zurich, Switzerland, September 6-12, 2014, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars, Eds. Berlin: Springer, vol. 8689, pp. 818–833.
  • T.Rahman, M. Chowdhury, and A. Khandakar, “COVID19 Radiography Database”, kaggle.com, Oct. 3, 2019. [Online]. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. [Accessed: May 8, 2022].
  • R. Caruana, “Multitask Learning,” Machine Learning, vol. 28, no. 1, pp. 41–75, 1997, doi: 10.1023/A:1007379606734 .
  • S. Thrun, “Is Learning The n-th Thing Any Easier Than Learning The First ?,” in Proc. of Advances in Neural Information Processing Systems 8, NIPS, Denver, CO, USA, November 27-30, 1995, D. S. Touretzky, M. C. Mozer and M. E. Hasselmo, Eds. United States:MIT Press, pp. 640–646, 1996.
  • J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-fei, “ImageNet : a Large-Scale Hierarchical Image Database,” in Proc. of 2009 IEEE Conference of Computer Vision Pattern Recognition., 20-25 June 2009, Miami, Florida, [Online]. Available: IEEE Xplore, https://doi.org/10.1109/CVPR.2009.5206848. [Accessed: 8 May 2022].
  • A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.
  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arxiv.org, Sep. 4, 2014. [Online]. Available: http://arxiv.org/abs/1409.1556. [Accessed: May 8, 2022].
  • S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding”, arxiv.org, Oct. 1, 2015. [Online]. Available: https://arxiv.org/abs/1510.00149. [Accessed: May 8, 2022].
  • A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications”, arxiv.org, Apr. 17, 2017. [Online]. Available: https://arxiv.org/abs/1704.04861. [Accessed: May 8, 2022].
  • J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger.”, arxiv.org, Dec. 25, 2016. [Online]. Available: https://arxiv.org/abs/1612.08242. [Accessed: May 8, 2022].
  • M. Lin, Q. Chen, and S. Yan, “Network In Network”, arxiv.org, Dec. 16, 2013. [Online]. Available: https://arxiv.org/abs/1312.4400. [Accessed: May 8, 2022].
  • Y. Li, W. Wang, and D. Yu, “Application of adaptive histogram equalization to x-ray chest images,” in Proc.of the Second International Conference on Optoelectronic Science and Engineering ’94, 15-18 August 1994, Beijing, China, [Online]. Available: SPIE Digital library, https://doi.org/10.1117/12.182056. [Accessed: 8 May 2022].
  • R. A. Hummel, “Image enhancement by histogram transformation,” Computer Graphics and Image Processing, vol. 6, no. 2, pp. 184–195, Apr. 1977, doi: 10.1016/S0146-664X(77)80011-7.
  • R.A. Hummel, “Histogram modification techniques,” Computer Graphics and Image Processing, vol. 4, no. 3, pp. 209–224, Sept. 1975, doi: 10.1016/0146-664X(75)90009-X.
  • D.J. Ketcham, “Real-time image enhancement techniques.,” Image Processing, vol. 74, pp. 120–125, 1976, doi: 10.1117/12.954708.
  • S.M. Pizer, “Intensity mappings for the display of medical images.,” Functional mapping of organ systems and other computer topics., pp. 205–217, 1981, doi: 10.1016/0146-664X(81)90006-X.
  • K. Zuiderveld, Graphics Gems IV:Contrast limited adaptive histogram equalization,” San Diego, CA, United States: Academic Press Professional, pp. 474–485, 1994.
  • S. M. Pizer, R. E. Johnston, J. P. Ericksen, B. C. Yankaskas, and K. E. Muller, “Contrast-limited adaptive histogram equalization: speed and effectiveness,” in Proc.of the First Conference on Visualization in Biomedical Computing, 1990, 22-25 May 1990, Atlanta, GA, USA, [Online]. Available: IEEE Xplore, https://doi.org/10.1109/VBC.1990.109340. [Accessed: 8 May 2022].
  • A. Narin, C. Kaya, and Z. Pamuk, “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks,” Pattern Analysis and Applications., vol. 24, no. 3, pp. 1207–1220, 2021, doi: 10.1007/s10044-021-00984-y.
  • F. S. Isabella Castiglioni et al., “Artificial Intelligence Applied on Chest X-Ray Can Aid in the Diagnosis of Covid-19 Infection”, medrxiv.org, Apr. 10, 2020. [Online]. Available: https://doi.org/10.1101/2020.04.08.20040907. [Accessed: May 8, 2022].
  • A. Haghanifar, M. M. Majdabadi, and S. Ko, “COVID-CXNet: Detecting covid-19 in frontal chest x-ray images using deep learning”, arxiv.org, Jun. 16, 2020. [Online]. Available: https://arxiv.org/abs/2006.13807. [Accessed: May 8, 2022].
  • D. Das, K. C. Santosh, and U. Pal, “Truncated inception net: COVID-19 outbreak screening using chest X-rays,” Physical and Engineering Sciences in Medicine, vol. 43, no. 3, pp. 915–925, Sep. 2020, doi: 10.1007/s13246-020-00888-x.
  • P. Saha, M. S. Sadi, and M. M. Islam, “EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers,” Informatics in Medicine Unlocked, vol. 22, p. 100505, 2021, doi: 10.1016/j.imu.2020.100505.
There are 36 citations in total.

Details

Primary Language English
Subjects Computer Software, Electrical Engineering
Journal Section Conference Paper
Authors

Gokhan Altan 0000-0001-7883-3131

Suleyman Serhan Narlı This is me 0000-0003-0162-9786

Publication Date September 1, 2022
Submission Date December 2, 2021
Acceptance Date April 29, 2022
Published in Issue Year 2022 Volume: 8 Issue: 2

Cite

IEEE G. Altan and S. S. Narlı, “CLAHE based Enhancement to Transfer Learning in COVID-19 Detection”, GJES, vol. 8, no. 2, pp. 406–416, 2022.

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