Geliştirilmiş EfficientNet-B0 mimarisi ile Helikobakter Pilorinin Teşhisi

Year 2024, Volume: 12 Issue: 2, 729 - 742, 29.06.2024

Demet Alıcı Karaca , Bahriye Baştürk Akay , Dervis Karaboga , Alper Baştürk , Özkan Ufuk Nalbantoğlu

https://doi.org/10.29109/gujsc.1441289

Abstract

Kansere bağlı ölümlerde önde gelen türlerden olan mide kanserine çevresel ve genetik birçok faktör sebebiyet verebilir. Başlıca risk faktörlerinden birisi ise midede gastrit ve ülsere neden olan helikobakter pilori bakteri virüsüdür. Bu virüsün tespit edilebilmesi için histopatolojik değerlendirme yapılmaktadır. Manuel yapılan bu işlem iş yükü, zaman kaybı ve subjektif değerlendirmeden kaynaklı patologlar arası görüş ayrılıklarına sebebiyet vermektedir. Tanı sürecini hızlandırmak ve hastaya zamanında tedavi uygulayarak yaşam süresini uzatmak amacıyla otomatik sistemlere ihtiyaç duyulmaktadır. Bu çalışmada son yıllarda başarımı artarak devam eden derin öğrenme mimarisi histopatolojik tam slayt görüntüden helikobakter pilorinin varlığını teşhis etmek için kullanılmaktadır. Mide biyopsi görüntülerini içeren halka açık DeepHP veri seti kullanılarak Helikobakter pilorinin tanısında uçtan-uca bir derin öğrenme modeli olanEfficientNet-B0 uygulanmıştır. Ayrıca, ağın özellik çıkarma yeteneğini geliştirmek amacıyla son zamanlarda literatüre sunulan çeşitli dikkat mekanizmaları (Etkili Kanal Dikkat, Frekans Kanal Dikkati Ağı, Kapılı Kanal Dönüşümü, Evrişimsel Blok Dikkat Modülü ve Basit, Parametresiz Dikkat Modülü) derin modele entegre edilerek model başarımı üzerindeki etkileri incelenmiştir. Yapılan analizler sonucunda, Frekans Kanal Dikkat Ağı entegre edilen EfficientNet-B0 mimarisinin, histopatolojik görüntülerden helikobakter pilorinin tanısında 0.99835 doğruluğa ulaştığı görülmüştür. Buna göre, önerilen model literatürde yer alan modellerin DeepHP veri seti üzerinde ürettiği sonuçlardan çok daha üstün bir sonuç üretmiştir ve hastalığın tanısında umut vaat edicidir.

Keywords

Helikobakter pilori, derin öğrenme, dikkat mekanizmaları, histopatoloji

The diagnosis of Helicobacter Pylori with the improved EfficientNet-B0 architecture

Abstract

Gastric cancer is among the leading cancers in cancer-related deaths. Many environmental and genetic factors can cause gastric cancer. However, one of the main risk factors is the helicobacter pylori bacterial virus, which causes gastritis and ulcers in the stomach. Diagnosis of helicobacter pylori is conducted by histopathological evaluation. However, this manual process creates differences of opinion among pathologists from subjective assessment in addition to workload and time loss. Automated systems are needed to speed up the diagnosis process and increase survival time by providing timely treatment to the patient. In recent years, deep learning models, which have proven successful in extracting meaningful results from images, have been used to diagnose the presence of helicobacter pylori from histopathological whole slide images. In this study, an end-to-end deep learning model, EfficientNet-B0, was applied in diagnosing Helicobacter pylori by using DeepHP, which contains gastric biopsy images, a public dataset. Various attention mechanisms introduced in recent years to improve the feature extraction ability of the network (Effective Channel Attention, Frequency Channel Attention Network, Gated Channel Transform, Convolutional Block Attention Module, and Simple, Parameter-Free Attention Module) were integrated into the deep model and their performances were examined. As a result of the analysis, the Frequency Channel Attention Network integrated into the EfficientNet-B0 architecture reached an accuracy of 0.99835 in diagnosing helicobacter pylori in the histopathological image. The result in the literature on the DeepHP dataset has been surpassed, and the proposed model is promising in diagnosing the disease.

Keywords

Helicobacter pylori, deep learning, attention mechanism, histopathology

References

• [1] P. Bhardwaj, G. Bhandari, Y. Kumar, and S. Gupta, “An Investigational Approach for the Prediction of Gastric Cancer Using Artificial Intelligence Techniques: A Systematic Review,” Archives of Computational Methods in Engineering. Springer Science and Business Media B.V., 2022. doi: 10.1007/s11831-022-09737-4.
• [2] H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA Cancer J Clin, vol. 71, no. 3, pp. 209–249, May 2021, doi: 10.3322/caac.21660.
• [3] T. Nishida et al., “Impact of time from diagnosis to chemotherapy in advanced gastric cancer: A Propensity Score Matching Study to Balance Prognostic Factors,” World J Gastrointest Oncol, vol. 11, no. 1, pp. 28–38, 2019, doi: 10.4251/wjgo.v11.i1.28.
• [4] R. Suzuki et al., “Aberrant methylation of microRNA-34b/c is a predictive marker of metachronous gastric cancer risk,” J Gastroenterol, vol. 49, no. 7, pp. 1135–1144, 2014, doi: 10.1007/s00535-013-0861-7.
• [5] M. A. Satolli, L. Buffoni, R. Spadi, and I. Roato, “Gastric Cancer: The Times they are a-changin’,” World J Gastrointest Oncol, vol. 7, no. 11, pp. 303–316, 2015, doi: 10.4251/wjgo.v7.i11.303.
• [6] P. Rawla and A. Barsouk, “Epidemiology of gastric cancer: Global trends, risk factors and prevention,” Przeglad Gastroenterologiczny, vol. 14, no. 1. pp. 26–38, 2019. doi: 10.5114/pg.2018.80001.
• [7] T. Matysiak-Budnik and F. Mégraud, “Helicobacter pylori infection and gastric cancer,” Eur J Cancer, vol. 42, no. 6, pp. 708–716, 2006, doi: 10.1016/j.ejca.2006.01.020.
• [8] J. Y. Lee and N. Kim, “Diagnosis of Helicobacter pylori by invasive test: Histology,” Ann Transl Med, vol. 3, no. 1, pp. 1–8, 2015, doi: 10.3978/j.issn.2305-5839.2014.11.03.
• [9] D. O. Faigel, M. Childs, E. E. Furth, A. Alavi, and D. C. Metz, “New Noninvasive Tests for Helicobacter pylori Gastritis: Comparison with Tissue-Based Gold Standard,” Digestive Diseases and Sciences, vol. 41, no. 4. Kluwer Academic/Plenum Publishers, pp. 740–748, 1996. doi: 10.1007/BF02213130.
• [10] O. Aydin, R. Egilmez, T. Karabacak, and A. Kanik, “Interobserver variation in histopathological assessment of Helicobacter pylori gastritis,” World J Gastroenterol, vol. 9, no. 10, pp. 2232–2235, 2003, doi: 10.3748/wjg.v9.i10.2232.
• [11] W. Dickey, B. Kenny, and J. McConnell, “Effect of proton pump inhibitors on the detection of,” Aliment Pharmacol Ther, vol. 10, no. 3, pp. 289–293, 1996.
• [12] O. C. Aktepe, I. H. Çiftçi, B. Şafak, I. Uslan, and F. H. Dilek, “Five methods for detection of Helicobacter pylori in the Turkish population,” World J Gastroenterol, vol. 17, no. 47, pp. 5172–5176, 2011, doi: 10.3748/wjg.v17.i47.5172.
• [13] J. K. Y. Hooi et al., “Global Prevalence of Helicobacter pylori Infection: Systematic Review and Meta-Analysis,” Gastroenterology, vol. 153, no. 2, pp. 420–429, 2017, doi: 10.1053/j.gastro.2017.04.022.
• [14] J. Potočnik, S. Foley, and E. Thomas, “Current and potential applications of artificial intelligence in medical imaging practice: A narrative review,” J Med Imaging Radiat Sci, vol. 54, no. 2, pp. 376–385, 2023, doi: 10.1016/j.jmir.2023.03.033.
• [15] M. Rana and M. Bhushan, “Machine learning and deep learning approach for medical image analysis: diagnosis to detection,” Multimed Tools Appl, vol. 82, no. 17, pp. 26731–26769, 2023, doi: 10.1007/s11042-022-14305-w.
• [16] S. Shafi and A. V. Parwani, “Artificial intelligence in diagnostic pathology,” Diagn Pathol, vol. 18, no. 1, pp. 1–12, 2023, doi: 10.1186/s13000-023-01375-z.
• [17] X. Jiang, Z. Hu, S. Wang, and Y. Zhang, “Deep Learning for Medical Image-Based Cancer Diagnosis,” Cancers (Basel), vol. 15, no. 14, 2023, doi: 10.3390/cancers15143608.
• [18] Y. DOĞAN, “Derin Öğrenme Yöntemleriyle Çapraz Veri Seti Değerlendirmesi Altında COVID-19 Tespiti,” Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol. 11, no. 3, pp. 813–823, 2023, doi: 10.29109/gujsc.1210343.
• [19] S. Banerji and S. Mitra, “Deep learning in histopathology: A review,” Wiley Interdiscip Rev Data Min Knowl Discov, vol. 12, no. 1, pp. 1–13, 2022, doi: 10.1002/widm.1439.
• [20] C. L. Srinidhi, O. Ciga, and A. L. Martel, “Deep neural network models for computational histopathology: A survey,” Med Image Anal, vol. 67, p. 101813, 2021, doi: 10.1016/j.media.2020.101813.
• [21] W. G. e. Gonçalves et al., “DeepHP: A New Gastric Mucosa Histopathology Dataset for Helicobacter pylori Infection Diagnosis,” Int J Mol Sci, vol. 23, no. 23, 2022, doi: 10.3390/ijms232314581.
• [22] S. Klein et al., “Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies,” BMC Gastroenterol, vol. 20, no. 1, pp. 1–11, 2020, doi: 10.1186/s12876-020-01494-7.
• [23] S. Zhou et al., “Deep learning assistance for the histopathologic diagnosis of Helicobacter pylori,” Intell Based Med, vol. 1–2, no. August, p. 100004, 2020, doi: 10.1016/j.ibmed.2020.100004.
• [24] Y. Yang, Y. Yang, Y. Yuan, J. Zheng, and Z. Zhongxi, “Detecting helicobacter pylori in whole slide images via weakly supervised multi-task learning,” Multimed Tools Appl, vol. 79, no. 35–36, pp. 26787–26815, 2020, doi: 10.1007/s11042-020-09185-x.
• [25] D. R. Martin, J. A. Hanson, R. R. Gullapalli, F. A. Schultz, A. Sethi, and D. P. Clark, “A deep learning convolutional neural network can recognize common patterns of injury in gastric pathology,” Arch Pathol Lab Med, vol. 144, no. 3, pp. 370–378, 2020, doi: 10.5858/arpa.2019-0004-OA.
• [26] Y. J. Lin, C. C. Chen, C. H. Lee, C. Y. Yeh, and Y. M. Jeng, “Two-tiered deep-learning-based model for histologic diagnosis of Helicobacter gastritis,” Histopathology, vol. 83, no. 5, pp. 771–781, 2023, doi: 10.1111/his.15018.
• [27] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
• [28] A. Kallipolitis, K. Revelos, and I. Maglogiannis, “Ensembling efficientnets for the classification and interpretation of histopathology images,” Algorithms, vol. 14, no. 10, 2021, doi: 10.3390/a14100278.
• [29] M. H. Guo et al., “Attention mechanisms in computer vision: A survey,” Computational Visual Media, vol. 8, no. 3. Tsinghua University, pp. 331–368, Sep. 01, 2022. doi: 10.1007/s41095-022-0271-y.
• [30] J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141, 2018, [Online]. Available: http://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html
• [31] F. Wang et al., “Residual attention network for image classification,” in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, pp. 6450–6458. doi: 10.1109/CVPR.2017.683.
• [32] S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, “CBAM: Convolutional block attention module,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, pp. 3–19. doi: 10.1007/978-3-030-01234-2_1.
• [33] J. Park, S. Woo, J. Y. Lee, and I. S. Kweon, “BAM: Bottleneck attention module,” in British Machine Vision Conference 2018, BMVC 2018, BMVA Press, 2019.
• [34] L. Yang, R. Y. Zhang, L. Li, and X. Xie, “SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks,” in Proceedings of Machine Learning Research, 2021, pp. 11863–11874. [Online]. Available: https://proceedings.mlr.press/v139/yang21o
• [35] Z. Qin, P. Zhang, F. Wu, and X. Li, “FcaNet: Frequency Channel Attention Networks,” in Proceedings of the IEEE International Conference on Computer Vision, 2021, pp. 763–772. doi: 10.1109/ICCV48922.2021.00082.
• [36] Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 11531–11539. doi: 10.1109/CVPR42600.2020.01155.
• [37] Z. Yang, L. Zhu, Y. Wu, and Y. Yang, “Gated Channel Transformation for Visual Recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 11791–11800. doi: 10.1109/CVPR42600.2020.01181.
• [38] L. Liang, Y. Zhang, S. Zhang, J. Li, A. Plaza, and X. Kang, “Fast Hyperspectral Image Classification Combining Transformers and SimAM-Based CNNs,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–19, 2023, doi: 10.1109/TGRS.2023.3309245.
• [39] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, International Conference on Learning Representations, ICLR, 2015.
• [40] H. M and S. M.N, “A Review on Evaluation Metrics for Data Classification Evaluations,” International Journal of Data Mining & Knowledge Management Process, vol. 5, no. 2, pp. 01–11, 2015, doi: 10.5121/ijdkp.2015.5201.