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

Year 2024, , 325 - 338, 11.07.2024

Serdar Abut

https://doi.org/10.21605/cukurovaumfd.1514012

Abstract

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.

Keywords

Artificial intelligence, COPD, Deep learning, Transfer learning, Medical image processing

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

Abstract

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.

Keywords

Yapay zeka, KOAH, Derin öğrenme, Transfer öğrenme, Medikal görüntü işleme

References

• 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.
• 21. Abut, S., Okut, H., Kallail, K.J., 2024. Paradigm Shift from Artificial Neural Networks (ANNs) to Deep Convolutional Neural Networks (DCNNs) in the Field of Medical Image Processing. Expert Systems with Applications, 244, 122983.
• 22. Abut, S., Okut, H., Zackula, R., James Kallail, K., 2024. Deep Neural Networks and Applications in Medical Research, in: D.M.J.D.-M. Ph, C.-M. Dr. Javier, M.-S. Mr. Luis, D. Dr. Robertas (Eds.) Deep Learning-Recent Findings and Research. IntechOpen, Rijeka, Ch. 1.
• 23. Abut, S., Okut, H., 2024. The Importance of Artificial Neural Networks in Decision Making for the Field of Medicine, in: G.A. Indrajit, Mittal; Hemlata, Jain (Ed.) The Future of Artificial Neural Networks. Nova Science, New York, 1-24.
• 24. Mouronte-Roibás, C., Fernández-Villar, A., Ruano-Raviña, A., Ramos-Hernández, C., Tilve-Gómez, A., Rodríguez-Fernández, P., Díaz, A.C.C., Vázquez-Noguerol, M.G., Fernández-García, S., Leiro-Fernández, V., 2018. Influence of the Type of Emphysema in the Relationship Between COPD and Lung Cancer. International Journal of Chronic Obstructive Pulmonary Disease, 13, 3563.
• 25. Humphries, S.M., Notary, A.M., Centeno, J.P., Strand, M.J., Crapo, J.D., Silverman, E.K., Lynch, D.A., Genetic Epidemiology of COPD Investigation, 2020. Deep Learning Enables Automatic Classification of Emphysema Pattern at CT. Radiology, 294, 434-444.
• 26. Germán, G., George, R.W., Raúl San José, E., 2018, Deep Learning for Biomarker Regression: Application to Osteoporosis and Emphysema on Chest CT Scans. Proc. SPIE, 10574
• 27. Campo, M.I., Pascau, J., Estépar, R.S.J., 2018. Emphysema Quantification on Simulated X-rays Through Deep Learning Techniques. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 273-276
• 28. Mohammadreza, N., David, B., 2019. Lung Tissue Characterization for Emphysema Differential Diagnosis Using Deep Convolutional Neural Networks. Proc. SPIE, 10950.
• 29. Boschetto, P., Miniati, M., Miotto, D., Braccioni, F., De Rosa, E., Bononi, I., Papi, A., Saetta, M., Fabbri, L.M., Mapp, C.E., 2003. Predominant Emphysema Phenotype in Chronic Obstructive Pulmonary Disease Patients. European Respiratory Journal, 21, 450.
• 30. Snoeck-Stroband, J.B., Lapperre, T.S., Gosman, M.M., Boezen, H.M., Timens, W., ten Hacken, N.H., Sont, J.K., Sterk, P.J., Hiemstra, P.S., Groningen Leiden Universities Corticosteroids in Obstructive Lung Disease Study, G., 2008. Chronic Bronchitis Sub-phenotype Within COPD: Inflammation in Sputum and Biopsies. Eur Respir J, 31, 70-77.
• 31. Makita, H., Nasuhara, Y., Nagai, K., Ito, Y., Hasegawa, M., Betsuyaku, T., Onodera, Y., Hizawa, N., Nishimura, M., Hokkaido, C.C.S.G., 2007. Characterisation of Phenotypes Based on Severity of Emphysema in Chronic Obstructive Pulmonary Disease. Thorax, 62, 932-937.
• 32. Ergen, B., Abut, S., 2013. Gender Recognition Using Facial Images. Proceedings of International Conference on Agriculture and Biotechnology IPCBEE, IACSIT Press, Singapore, 60(22), 112-117
• 33. Masmoudi, A.D., Masmoudi, D.S., 2010. Implementation of a Fingerprint Recognition System Using LBP Descriptor. Journal of Testing and Evaluation, 38, 369-382.
• 34. Shams, M., Rashad, M., Nomir, O., El-Awady, R., 2011. Iris Recognition Based on LBP and Combined LVQ Classifier. ArXiv, abs/1111. 1562.
• 35. Haibo, W., Angel, C.-R., Ajay, B., Hannah, G., Natalie, S., Mike, F., John, T., Fabio, G., Anant, M., 2014, Cascaded Ensemble of Convolutional Neural Networks and Handcrafted Features for Mitosis Detection. Proc.SPIE, 9041.
• 36. Lin, W., Hasenstab, K., Moura Cunha, G., Schwartzman, A., 2020. Comparison of Handcrafted Features and Convolutional Neural Networks for Liver MR Image Adequacy Assessment. Sci Rep, 10, 20336.
• 37. Abut, S., Doğanay, F., Yeşilova, A., Buğa, S., 2021. Analysis of Pulmonary Function Test Results By Using Gaussian Mixture Regression Model. Journal of Clinical Medicine of Kazakhstan, 18, 23-29.
• 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
• 40. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A., 2016. Learning Deep Features for Discriminative Localization, 2921-2929
• 41. Oquab, M., Bottou, L., Laptev, I., Sivic, J., 2015. Is Object Localization for Free?-Weakly-Supervised Learning with Convolutional Neural Networks, 685-694
• 42. Perez, L., Wang, J., 2017. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. ArXiv, abs/1712.04621.
• 43. Shorten, C., Khoshgoftaar, T.M., 2019. A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6, 60.
• 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.
• 47. Hubel, D.H., Wiesel, T.N., 1968. Receptive Fields and Functional Architecture of Monkey Striate Cortex. J Physiol, 195, 215-243.
• 48. Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y., 2003. Subject Independent Facial Expression Recognition with Robust Face Detection Using a Convolutional Neural Network. Neural Netw, 16, 555-559.
• 49. Jaiswal, R., Rao, A.G., Shukla, H.P., 2010. Image Enhancement Techniques Based on Histogram Equalization. International Journal of Electrical and Electronics Engineering, 1, 69-78.
• 50. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
• 51. LeCun, Y., Fu Jie, H., Bottou, L., 2004. Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2004., 2 II-104, 102.
• 52. Griffin, G., Holub, A., Perona, P., 2007. Caltech-256 Object Category Dataset, 1-20.
• 53. Ciregan, D., Meier, U., Schmidhuber, J., 2012, Multi-column Deep Neural Networks for Image Classification. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3642-3649
• 54. Deng, J., Dong, W., Socher, R., Li, L., Kai, L., Li, F.-F., 2009. ImageNet: A Large-scale Hierarchical Image Database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248-255
• 55. Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T., 2008. LabelMe: A Database and Web-based Tool for Image Annotation. International Journal of Computer Vision, 77, 157-173.
• 56. Zeiler, M.D., Fergus, R., 2014. Visualizing and Understanding Convolutional Networks. Computer Vision-ECCV 2014, Springer International Publishing, Cham, 818-833
• 57. Ventura, D., Warnick, S., 2007. A Theoretical Foundation for Inductive Transfer. Brigham Young University, College of Physical and Mathematical Sciences, 19.
• 58. Jeff, D., Yangqing, J., Oriol, V., Judy, H., Ning, Z., Eric, T., Trevor, D., 2014. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. PMLR, 647-655.
• 59. Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S., 2014. CNN Features Off-the-shelf: an Astounding Baseline for Recognition, 806-813
• 60. Yosinski, J., Clune, J., Bengio, Y., Lipson, H., 2014. How Transferable are Features in Deep Neural Networks? ArXiv, abs/1411.1792.
• 61. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015, Going Deeper with Convolutions, 1-9.
• 62. Simonyan, K., Zisserman, A., 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs/1409.1556.
• 63. He, K., Zhang, X., Ren, S., Sun, J., 2016, Deep Residual Learning for Image Recognition, 770-778.
• 64. Sammani, A., Bagheri, A., van der Heijden, P.G.M., Te Riele, A., Baas, A.F., Oosters, C.A. J., Oberski, D., Asselbergs, F.W., 2021. Automatic Multilabel Detection of ICD10 Codes in Dutch Cardiology Discharge Letters Using Neural Networks. NPJ Digit Med, 4, 37.
• 65. Bressem, K.K., Adams, L.C., Erxleben, C., Hamm, B., Niehues, S.M., Vahldiek, J.L., 2020. Comparing Different Deep Learning Architectures for Classification of Chest Radiographs. Scientific Reports, 10, 13590.
• 66. Ofer, D., Ohad, S., 2010. Multiclass-Multilabel Classification with More Classes than Examples. PMLR, 137-144.
• 67. de Carvalho, A.C.P.L.F., Freitas, A.A., 2009. A Tutorial on Multi-label Classification Techniques, in: A. Abraham, A.-E. Hassanien, V. Snášel (Eds.) Foundations of Computational Intelligence Volume 5, Springer Berlin Heidelberg, Berlin, Heidelberg, 177-195.
• 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
• 72. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171-4186, Minneapolis, Minnesota.
• 73. Yang, Q., Zhang, Y., Dai, W., Pan, S.J., 2020. Transfer Learning, Cambridge University Press, Cambridge.
• 74. Atasever, S., Azginoglu, N.U.H., Terzi, D.S., Terzi, R., 2023. A Comprehensive Survey of Deep Learning Research on Medical Image Analysis with Focus on Transfer Learning, Clinical Imaging, 94, 18-41.