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AI-Based Model Design for Prediction of COPD Grade from Chest X-Ray Images: A Model Proposal (COPD-GradeNet)

Yıl 2024, Cilt: 39 Sayı: 2, 325 - 338, 11.07.2024
https://doi.org/10.21605/cukurovaumfd.1514012

Öz

Chronic Obstructive Pulmonary Disease (COPD) ranks high among the leading causes of death, particularly in middle- and low-income countries. Early diagnosis of COPD is challenging, with limited diagnostic methods currently available. In this study, a artificial intelligence model named COPD-GradeNet is proposed to predict COPD grades from radiographic images. However, the model has not yet been tested on a dataset. Obtaining a dataset including spirometric test results and chest X-ray images for COPD is a challenging process. Once the proposed model is tested on an appropriate dataset, its ability to predict COPD grades can be evaluated and implemented. This study may guide future research and clinical applications, emphasizing the potential of artificial intelligence-based approaches in the diagnosis of COPD.

Kaynakça

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Akciğer Grafilerinden KOAH Derecesinin Tahmin Edilmesi için Yapay Zeka Temelli Model Tasarımı: Bir Model Önerisi (COPD-GradeNet)

Yıl 2024, Cilt: 39 Sayı: 2, 325 - 338, 11.07.2024
https://doi.org/10.21605/cukurovaumfd.1514012

Öz

Kronik Obstrüktif Akciğer Hastalığı (KOAH), özellikle orta ve düşük gelirli ülkelerde ölüm nedenleri arasında üst sıralarda yer alır. KOAH'ın erken teşhisi zordur ve mevcut tanı yöntemleri sınırlıdır. Bu çalışmada, radyografi görüntülerinden KOAH derecelerini tahmin etmek için bir yapay zeka modeli olan COPD-GradeNet önerilmektedir. Ancak, model henüz bir veri seti üzerinde test edilmemiştir. KOAH'ın spirometrik test sonuçları ve akciğer röntgen görüntüleri gibi bir veri setinin elde edilmesi zorlu bir süreçtir. Önerilen modelin uygun bir veri setiyle test edilmesi halinde, KOAH derecelerini tahmin etme yeteneğinin değerlendirilip uygulanabileceği düşünülmektedir. Bu çalışma, gelecekteki araştırmalara ve klinik uygulamalara yol gösterebilir, KOAH teşhisinde yapay zeka tabanlı yaklaşımların potansiyelini vurgulayabilir.

Kaynakça

  • 1. Roman-Rodriguez, M., Kaplan, A., 2021. GOLD 2021 Strategy Report: Implications for Asthma-COPD Overlap. Int J Chron Obstruct Pulmon Dis, 16, 1709-1715.
  • 2. Halpin, D.M.G., Criner, G.J., Papi, A., Singh, D., Anzueto, A., Martinez, F.J., Agusti, A.A., Vogelmeier, C.F., 2021. Global Initiative for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease. The 2020 GOLD Science Committee Report on COVID-19 and Chronic Obstructive Pulmonary Disease, Am J Respir Crit Care Med, 203, 24-36.
  • 3. GOLD, 2021 Global Strategy for Prevention. Diagnosis and Management of COPD, 2021. 1-164.
  • 4. Willer, K., Fingerle, A.A., Gromann, L.B., De Marco, F., Herzen, J., Achterhold, K., Gleich, B., Muenzel, D., Scherer, K., Renz, M., Renger, B., Kopp, F., Kriner, F., Fischer, F., Braun, C., Auweter, S., Hellbach, K., Reiser, M.F., Schroeter, T., Mohr, J., Yaroshenko, A., Maack, H.I., Pralow, T., van der Heijden, H., Proksa, R., Koehler, T., Wieberneit, N., Rindt, K., Rummeny, E.J., Pfeiffer, F., Noel, P.B., 2018. X-ray Dark-Field Imaging of the Human Lung-A Feasibility Study on a Deceased Body. PLoS One, 13, e0204565.
  • 5. Bech, M., Bunk, O., Donath, T., Feidenhans'l, R., David, C., Pfeiffer, F., 2010. Quantitative X-ray Dark-Field Computed Tomography. Physics in Medicine and Biology, 55, 5529-5539.
  • 6. Pfeiffer, F., Bech, M., Bunk, O., Kraft, P., Eikenberry, E.F., Bronnimann, C., Grunzweig, C., David, C., 2008. Hard-X-ray Dark-Field Imaging Using a Grating Interferometer, Nat Mater, 7, 134-137.
  • 7. Meinel, F.G., Yaroshenko, A., Hellbach, K., Bech, M., Muller, M., Velroyen, A., Bamberg, F., Eickelberg, O., Nikolaou, K., Reiser, M.F., Pfeiffer, F., Yildirim, A.O., 2014. Improved Diagnosis of Pulmonary Emphysema Using in Vivo Dark-Field Radiography. Invest Radiol, 49, 653-658.
  • 8. Baker, N., Lu, H., Erlikhman, G., Kellman, P.J., 2018. Deep Convolutional Networks do Not Classify Based on Global Object Shape, PLOS Computational Biology, 14, e1006613.
  • 9. Tuli, S., Dasgupta, I., Grant, E., Griffiths, T.L., 2021. Are Convolutional Neural Networks or Transformers More Like Human Vision?, arXiv preprint arXiv:2105.07197.
  • 10. Afshar, P., Heidarian, S., Enshaei, N., Naderkhani, F., Rafiee, M.J., Oikonomou, A., Fard, F.B., Samimi, K., Plataniotis, K.N., Mohammadi, A., 2021. COVID-CT-MD, COVID-19 Computed Tomography Scan Dataset Applicable in Machine Learning and Deep Learning. Scientific Data, 8, 121.
  • 11. Wang, G., Liu, X., Shen, J., Wang, C., Li, Z., Ye, L., Wu, X., Chen, T., Wang, K., Zhang, X., Zhou, Z., Yang, J., Sang, Y., Deng, R., Liang, W., Yu, T., Gao, M., Wang, J., Yang, Z., Cai, H., Lu, G., Zhang, L., Yang, L., Xu, W., Wang, W., Olvera, A., Ziyar, I., Zhang, C., Li, O., Liao, W., Liu, J., Chen, W., Chen, W., Shi, J., Zheng, L., Zhang, L., Yan, Z., Zou, X., Lin, G., Cao, G., Lau, L. L., Mo, L., Liang, Y., Roberts, M., Sala, E., Schonlieb, C.B., Fok, M., Lau, J.Y., Xu, T., He, J., Zhang, K., Li, W., Lin, T., 2021. A Deep-learning Pipeline for the Diagnosis and Discrimination of Viral, Non-viral and COVID-19 Pneumonia from Chest X-ray Images. Nat Biomed Eng, 5, 509-521.
  • 12. Elaziz, M.A., Hosny, K.M., Salah, A., Darwish, M.M., Lu, S., Sahlol, A.T., 2020. New Machine Learning Method for Image-Based Diagnosis of COVID-19. PLoS One, 15, e0235187.
  • 13. Zargari Khuzani, A., Heidari, M., Shariati, S. A., 2021. COVID-Classifier: an Automated Machine Learning Model to Assist in the Diagnosis of COVID-19 Infection in Chest X-ray Images. Sci Rep, 11, 9887.
  • 14. Patel, R.K., Kashyap, M., 2022. Automated Diagnosis of COVID Stages from Lung CT Images Using Statistical Features in 2-dimensional Flexible Analytic Wavelet Transform. Biocybern Biomed Eng, 42, 829-841.
  • 15. Deniz, C.M., Xiang, S., Hallyburton, R.S., Welbeck, A., Babb, J.S., Honig, S., Cho, K., Chang, G., 2018. Segmentation of the Proximal Femur from MR Images Using Deep Convolutional Neural Networks. Sci Rep, 8, 16485.
  • 16. Jakaite, L., Schetinin, V., Hladuvka, J., Minaev, S., Ambia, A., Krzanowski, W., 2021. Deep Learning for Early Detection of Pathological Changes in X-ray Bone Microstructures: Case of Osteoarthritis. Sci Rep, 11, 2294.
  • 17. Park, D.J., Park, M.W., Lee, H., Kim, Y.J., Kim, Y., Park, Y.H., 2021. Development of Machine Learning Model for Diagnostic Disease Prediction Based on Laboratory Tests. Sci Rep, 11, 7567.
  • 18. Tang, S., Ghorbani, A., Yamashita, R., Rehman, S., Dunnmon, J.A., Zou, J., Rubin, D.L., 2021. Data Valuation for Medical Imaging Using Shapley Value and Application to a Large-scale Chest X-ray Dataset. Sci Rep, 11, 8366.
  • 19. Chen, Y., Wan, Y., Pan, F., 2023. Enhancing Multi-disease Diagnosis of Chest X-rays with Advanced Deep-learning Networks in Real-World Data. J Digit Imaging, 36, 1332-1347.
  • 20. Shen, Y., Wu, N., Phang, J., Park, J., Liu, K., Tyagi, S., Heacock, L., Kim, S.G., Moy, L., Cho, K., Geras, K.J., 2021. An Interpretable Classifier for High-resolution Breast Cancer Screening Images Utilizing Weakly Supervised Localization. Med Image Anal, 68, 101908.
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  • 38. Kuru, L.İ., Günay, O., Palaci, H., Yarar, O., 2019. Bilgisayarlı Tomografilerde Hastanın Aldığı Efektif Radyasyon Dozunun Belirlenmesi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21, 436-443.
  • 39. Işık, Z., Selçuk, H., Albayram, S., 2010. Bilgisayarlı Tomografi ve Radyasyon. Klinik Gelişim, 23, 16-18
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  • 42. Perez, L., Wang, J., 2017. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. ArXiv, abs/1712.04621.
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  • 44. Huang, C.C., Nguyen, M.H., 2019. X-Ray Enhancement Based on Component Attenuation, Contrast Adjustment, and Image Fusion. IEEE Trans Image Process, 28, 127-141.
  • 45. Liu, Y., Zhang, P.C., Gui, Z.G., 2021. An Enhancement Framework Based on Gradient Domain Tone Mapping and Fuzzy Logical for X-ray Image of Complex Workpiece. Ndt & E International, 121, 102455.
  • 46. Fukushima, K., Miyake, S., 1982. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition, Competition and Cooperation in Neural Nets. Springer Berlin Heidelberg, Berlin, Heidelberg, 267-285.
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  • 66. Ofer, D., Ohad, S., 2010. Multiclass-Multilabel Classification with More Classes than Examples. PMLR, 137-144.
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  • 68. Xu, Q., Yang, Q., 2011. A Survey of Transfer and Multitask Learning in Bioinformatics. Journal of Computing Science and Engineering, 5, 257-268.
  • 69. Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R. M., 2016. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans Med Imaging, 35, 1285-1298.
  • 70. Liu, B., Wei, Y., Zhang, Y., Yan, Z.X., Yang, Q., 2018. Transferable Contextual Bandit for Cross-Domain Recommendation. Thirty-Second Aaai Conference on Artificial Intelligence/Thirtieth Innovative Applications of Artificial Intelligence Conference/Eighth Aaai Symposium on Educational Advances in Artificial Intelligence, 32, 3619-3626.
  • 71. Tai, L., Paolo, G., Liu, M., 2017. Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation,. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 31-36
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Toplam 74 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme
Bölüm Makaleler
Yazarlar

Serdar Abut Bu kişi benim 0000-0002-6617-6688

Yayımlanma Tarihi 11 Temmuz 2024
Gönderilme Tarihi 27 Mart 2024
Kabul Tarihi 27 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 39 Sayı: 2

Kaynak Göster

APA Abut, S. (2024). AI-Based Model Design for Prediction of COPD Grade from Chest X-Ray Images: A Model Proposal (COPD-GradeNet). Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 39(2), 325-338. https://doi.org/10.21605/cukurovaumfd.1514012