X-ışınlı Göğüs İmgelerini Kullanarak Solunum Yolu Hastalıklarının Tespitinde Kuantum Transfer Öğrenme Modelinin Rolü

Abstract

Solunum yolu hastalıkları çeşitli kanallar vasıtasıyla insanların solunum yollarına bulaşan; virüs ve bakteri gibi mikro organizmaların neden olduğu hastalıklardır. Bu canlılar vücudun bağışıklık sistemini zayıflatarak enfeksiyon oluşmasına yol açar ve bireyde kulak, burun, boğaz, solunum borusu ve akciğer gibi organlarda çoğalabilirler. Bunun sonucunda; zatürre, Ciddi Akut Solunum Sendromu (SARS), Orta Doğu Solunum Sendromu (MERS), Korona Virüs Hastalığı (COVID-19) gibi hastalıkların oluşmasına neden olabilmektedir ve erken müdahale alınmadığı takdirde hastaların ölümüne yol açabilmektedir. Bu çalışmada Kuantum modeli, derin öğrenme modeli ile yoğrularak farklı bir öğrenme yaklaşımı önerilmiştir. Bu model çeşitli kütüphane yazılımcıları tarafından verilen destekler ile gelişimini sürdürmektedir. Çalışmada kullanılan veri seti, solunum hastalıkları ve normal X-ışınları görüntülerinden oluşmaktadır. Deney analizinde, Kuantum Transfer Öğrenme (KTÖ) modeli kullanılarak veri setinin eğitimi gerçekleştirildi ve analiz sonuçlarından elde edilen doğruluk %92,50'ydi. Sonuç olarak, kuantum öğrenme modelinin derin öğrenme modelleri gibi umut verici sonuçlar verdiği bu çalışmada gözlemlendi.

Keywords

• Derin öğrenme
• Kuantum transfer öğrenme
• Solunum hastalıkları
• X-ışınları

The Role of Quantum Transfer Learning Model in the Detection of Respiratory Diseases Using X-ray Chest Images

Abstract

Respiratory diseases are transmitted to the respiratory tract of people through various channels; diseases caused by micro-organisms such as viruses and bacteria. These creatures weaken the body's immune system, leading to the formation of infection, and can reproduce in the individual in organs such as the ear, nose, throat, respiratory tract and lung. As a result; it can cause diseases such as "pneumonia, Serious Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), Corona Virus Disease (COVID-19)" and can lead to the death of patients if early intervention is not received. In this study, a different learning approach is proposed by combining the quantum model and the deep learning model. This model continues its development with the support provided by various library software developers. The dataset used in the study consists of respiratory diseases and normal X-ray images. In the experimental analysis, the dataset was trained using the Quantum Transfer Learning (QTL) model and the accuracy rate obtained from the analysis results was 92.50%. As a result, it was observed in this study that the Quantum approach gave promising results like deep learning models.

Keywords

• Deep learning
• Quantum transfer learning
• Respiratory diseases
• X-ray

References

1. [1] D. Kim, Z. Chen, L.-F. Zhou, and S.-X. Huang, “Air pollutants and early origins of respiratory diseases,” Chronic Dis. Transl. Med., vol. 4, no. 2, pp. 75–94, 2018.
2. [2] Z. Shi and A. T. Gewirtz, “Together forever: bacterial-viral ınteractions in ınfection and ımmunity,” Viruses, vol. 10, no. 3, pp. 122, Mar. 2018.
3. [3] N. Petrosillo, G. Viceconte, O. Ergonul, G. Ippolito, and E. Petersen, “COVID-19, SARS and MERS: are they closely related?,” Clin. Microbiol. Infect., vol. 26, no. 6, pp. 729–734, Jun. 2020.
4. [4] Wikipedia. (2021, Jun 28). COVID-19 pandemic by country and territory [Online]. Available: https://en.wikipedia.org/wiki/COVID-19_pandemic_by_country_and_territory.
5. [5] W. H. Man, W. A. A. de Steenhuijsen Piters and D. Bogaert, “The microbiota of the respiratory tract: gatekeeper to respiratory health,” Nat. Rev. Microbiol., vol. 15, no. 5, pp. 259–270, 2017.
6. [6] Amisha, P. Malik, M. Pathania, and V. K. Rathaur, “Overview of artificial intelligence in medicine,” J. Fam. Med. Prim. Care, vol. 8, no. 7, pp. 2328–2331, 2019.
7. [7] E. Bercovich and M. C. Javitt, “Medical imaging: from roentgen to the digital revolution, and beyond,” Rambam Maimonides Med. J., vol. 9, no. 4, pp. e0034, Oct. 2018.
8. [8] A. AlMoammar, L. AlHenaki, and H. Kurdi, “Selecting accurate classifier models for a MERS-CoV dataset,” Intell. Syst. Appl. Proc. 2018 Intell. Syst. Conf., vol. 1, no. 868, pp. 1070–1084, 2018.

9. [9] E. Hemdan, M. A. Shouman and M. Karar. (2021, Jun 16). Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images [Online]. Available: https://arxiv.org/abs/2003.11055
10. [10] X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Q. Ni, Y. Chen, J. Su, G. Lang, Y. Li, H. Zhao, J. Liu, K. Xu, L. Ruan, J. Sheng, Y. Qiu, W. Wu, T. Liang and L. Li, “A deep learning system to screen novel coronavirus disease 2019 pneumonia,” Engineering, vol. 6, no. 10, pp. 1122–1129, 2020.
11. [11] X. Mei, H. Lee, K. Diao, M. Huang, B. Lin, C. Liu, Z. Xie, Y. Ma, P. Robson, M. Chung, A. Bernheim, V. Mani, C. Calcagno, K. Li, S. Li, H. Shan, J. Lv, T. Zhao, J. Xia, Q. Long, S. Steinberger, A. Jacobi, T. Deyer, M. Luksza, F. Liu, B. P. Little, Z. A. Fayad and Y. Yang, “Artificial intelligence–enabled rapid diagnosis of patients with COVID-19,” Nat. Med., 2020, doi: 10.1101/2020.04.12.20062661.
12. [12] E.H. Houssein, Z. Abohashima, M. Elhoseny and W.M. Mohamed. (2021, July 10). Hybrid quantum convolutional neural netarxworks model for COVID-19 prediction using chest X-Ray images [Online]. Available: https://arxiv.org/abs/2102.06535v1.
13. [13] E. Acar and İ. Yılmaz, “COVID-19 detection on IBM quantum computer with classical quantum transfer learning,” Turkish J. Electr. Eng. Comput. Sci, vol. 29, pp. 46–61, 2021.
14. [14] W.H. Khoong, (2021, Jun 16). COVID-19 x-ray dataset (train & test sets) | Kaggle [Online]. Available: https://www.kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets.
15. [15] X. Zhang, L. Fu, M. Karkee, M. D. Whiting and Q. Zhang, “Canopy segmentation using ResNet for mechanical harvesting of apples,” IFAC-PapersOnLine, vol. 52, no. 30, pp. 300–305, 2019.
16. [16] C. R. Alimboyong and A. A. Hernandez, “An improved deep neural network for classification of plant seedling images,” in 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), Penang, Malezya, 2019, pp. 217–222.
17. [17] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks,” Comput. Biol. Med., vol. 121, pp. 103795, Jun. 2020.
18. [18] S. Maharjan, A. Alsadoon, P. W. C. Prasad, T. Al-Dalain, and O. H. Alsadoon, “A novel enhanced softmax loss function for brain tumour detection using deep learning,” J. Neurosci. Methods, vol. 330, pp. 108520, 2020.
19. [19] A. Mari, N. Killoran, and J. Izaac, (2021, Jun 10). Quantum transfer learning [Online]. Available: https://github.com/XanaduAI/quantum-transferlearning/blob/master/c2q_transfer_learning_ants_bees.ipynb.
20. [20] S. Ornes, “News Feature: Quantum effects enter the macroworld,” Proc. Natl. Acad. Sci., vol. 116, no. 45, pp. 22413 LP – 22417, Nov. 2019.
21. [21] M. Andrea, B. R.Thomas , I. Josh, S. Maria and K. Nathan. (2021, July 1). Transfer learning in hybrid classical quantum neural networks [Online]. Available: https://arxiv.org/abs/1912.08278v2.
22. [22] S. Garg and G. Ramakrishnan. (2021, July 10). Advances in quantum deep learning: an overview [Online]. Available: https://arxiv.org/abs/2005.04316.
23. [23] K. Beer, D. Bondarenko, T.Farrelly, T. J. Osborne, R. Salzmann and R. Wolf, “Training deep quantum neural networks,” Nat. Commun., vol. 11, no. 1, pp. 808, 2020.
24. [24] P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler, “Benign overfitting in linear regression,” Proc. Natl. Acad. Sci., pp. 201907378, Apr. 2020.
25. [25] H. Zhong, Z. Chen, C. Qin, Z. Huang, V. W. Zheng, T. Xu and E. Chen., “Adam revisited: a weighted past gradients perspective,” Front. Comput. Sci., vol. 14, no. 5, pp. 145309, 2020.
26. [26] F. Demir, A. Şengür, V. Bajaj, and K. Polat, “Towards the classification of heart sounds based on convolutional deep neural network,” Heal. Inf. Sci. Syst., vol. 7, no. 1, pp. 16, 2019.
27. [27] Z. Yang, C. Wang, Z. Zhang, and J. Li, “Mini-batch algorithms with online step size,” Knowledge-Based Syst., vol. 165, pp. 228–240, 2019.
28. [28] TensofFlow, (2021, Jun 4). TensorFlow quantum [Online]. Available: https://www.tensorflow.org/quantum.