Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach

GÜLDOĞAN, Emek; Ucuzal, Hasan; ÇOLAK, Cemil; KÜÇÜKAKÇALI, ZEYNEP

doi:10.5336/biostatic.2021-82416

Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach

Emek GÜLDOĞAN, (İnönü Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi Anabilim Dalı, Malatya, Türkiye)

Zeynep TUNÇ, (İnönü Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi Anabilim Dalı, Malatya, Türkiye)

Hasan UCUZAL, (İnönü Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi Anabilim Dalı, Malatya, Türkiye)

Cemil ÇOLAK (İnönü Üniversitesi, Tıp Fakültesi, Biyoistatistik ve Tıp Bilişimi Anabilim Dalı, Malatya, Türkiye)

Türkiye Klinikleri Biyoistatistik Dergisi

3 1

Yıl: 2021 Cilt: 13 Sayı: 2 Sayfa Aralığı: 147 - 159 Metin Dili: İngilizce DOI: 10.5336/biostatic.2021-82416 İndeks Tarihi: 17-02-2022

Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach

Öz:

Objective: Automatic machine learning methods developed by employing deep learning approaches have been the focus of numerous studies nowadays. The objective of the current study is to design a web-based software that is used in the classification of tissue samples in colorectal cancer, based on eight different histopathological tissue types, to support physicians for the clinical diagnosis of colorectal cancer, and thus to enable physicians to make quick and accurate decisions. Material and Methods: An open-access data set (DOI: 10.5281/zenodo.53169) consisting of 5,000 histopathological images, including different histopathological tissue types of colorectal cancer, was used in the present study. Keras-based AutoKeras library was applied to classify the histopathological tissue types of colorectal cancer. Appropriate python language libraries were employed in the development of the web-based software. A deep learning-based model was constructed to predict eight histopathological tissue types of colorectal cancer. Results: The highest metric values among the performance criteria achieved for different tissue types of colorectal cancer were calculated for adipose type, and we found that accuracy was 0.996, sensitivity 0.992, specificity 0.996, precision 0.974, recall 0.992, and F1-score 0.983, respectively. This research differs from other studies in that it includes open access software. Conclusion: The web software based on the model proposed in this study provided promising predictions in classifying different tissue types from histopathological images of colorectal cancer. Thanks to the proposed software, the tissue types of colorectal cancer are easily understood by medical professionals and other healthcare workers. Hence, the workload of medical professionals can be reduced, and a faster consultation system can be formed.

Anahtar Kelime:

Derin Öğrenme Yaklaşımı Kullanılarak Histolojik Görüntülere Dayalı Kolorektal Kanserin Farklı Doku Tiplerinin Saptanması

Öz:

Amaç: Derin öğrenme algoritmaları kullanılarak geliştirilen otomatik makine öğrenme algoritmaları, son zamanlarda birçok çalışmanın ilgi odağı olmuştur. Bu çalışmanın amacı, kolorektal kansere ilişkin histopatolojik görüntülere ait doku örneklerinin bilinen 8 farklı histopatolojik doku tiplerine göre sınıflandırmasını yapabilecek, kolorektal kanser tanısında hekimlere klinik destek verebilecek ve bu sayede hekimlerin hızlı ve doğru karar verebilmelerine imkân sağlayabilecek web tabanlı bir yazılım geliştirmektir. Gereç ve Yöntemler: Bu çalışmada, kolorektal kansere ait farklı histopatolojik doku tiplerini içeren 5.000 histopatolojik görüntüden oluşan açık erişimli veri seti (DOI: 10.5281/zenodo.53169) kullanılmıştır. Geliştirilen yazılımdaki derin öğrenme algoritmasının oluşturulmasında Python programlama dilinde kullanılan Keras/AutoKeras kütüphanesi, kolorektal kansere ait histopatolojik doku tiplerini sınıflandırmak için uygulanmıştır. Web tabanlı yazılımın geliştirilmesinde Python dili kütüphaneleri kullanıldı. Kolorektal kanserin 8 histopatolojik doku tipini tahmin etmek için derin öğrenmeye dayalı bir model oluşturuldu. Bulgular: Farklı doku tipleri için hesaplanan performans ölçütleri arasında en yüksek metrik değerleri adipose için hesaplanmış olup sırasıyla; doğruluk 0,996, duyarlılık 0,992, özgüllük 0,996, kesinlik 0,974, geri çekme 0,992 ve F1-skoru 0,983 olarak bulunmuştur. Bu araştırma, açık erişim yazılımı içermesi ile diğer çalışmalardan farklıdır. Sonuç: Elde edilen deneysel bulgular, geliştirilen bu yazılımın kolorektal kansere ait 8 doku türünün tespiti ve teşhisinde kullanılabileceğini göstermektedir. Geliştirilen yazılım sayesinde kolorektal kansere ait doku türlerinin tıp uzmanları ve diğer sağlık çalışanları tarafından kolayca anlaşılması sağlanmaktadır. Bu sayede tıp uzmanları ve diğer sağlık çalışanlarının iş yükü azalmış olur ve hızlı bir danışma sistemi oluşturulmuş olur.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

1. Yardım N, Mollahaliloğlu S, Bora Başara B. Türkiyede kanser durumu ve uluslararası göstergeler ile uyumun değerlendirilmesi. Tuncer AM, Özgül N, Olcayto E, Gültekin, editörler. Türkiye'de Kanser Kontrolü. Ankara: Koza Matbaacılık; 2009. p.51-63.
2. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893-917.
3. Siegel RL, Sahar L, Robbins A, Jemal A. Where can colorectal cancer screening interventions have the most impact? Cancer Epidemiol Biomarkers Prev. 2015;24(8):1151-6.
4. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55(2):74-108.
5. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66(1):7-30.
6. Ciombor KK, Wu C, Goldberg RM. Recent therapeutic advances in the treatment of colorectal cancer. Annual Review of Medicine. 2015;66:83- 95.
7. Basavanhally A, Yu E, Xu J, Ganesan S, Feldman M, Tomaszewski J, et al. Incorporating domain knowledge for tubule detection in breast histopathology using O'Callaghan neighborhoods. Medical Imaging. 2011: Computer-Aided Diagnosis. 2011.
8. Paramanandam M, O'Byrne M, Ghosh B, Mammen JJ, Manipadam MT, Thamburaj R, et al. Automated segmentation of nuclei in breast cancer histopathology images. PLoS One. 2016;11(9):e0162053.
9. Bayramoglu N, Kannala J, Heikkilä J. Deep learning for magnification independent breast cancer histopathology image classification. 23rd International conference on pattern recognition. 2016; December 4-8; Cancun, Mexico.
10. Talo M, Baloglu UB, Yıldırım Ö, Acharya UR. Application of deep transfer learning for automated brain abnormality classification using MR images. Cognitive Systems Research. 2019;54:176-88.
11. Kaya U, Yılmaz A, Dikmen Y. [Deep Learning Methods used in the field of Health]. Avrupa Bilim ve Teknoloji Dergisi. 2019(16):792-808.
12. Kather JN, Weis C-A, Bianconi F, Melchers SM, Schad LR, Gaiser T, et al. Multi-class texture analysis in colorectal cancer histology. Scientific Reports. 2016;6(1):27988.
13. Xu J, Luo X, Wang G, Gilmore H, Madabhushi A. A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing. 2016;191:214-23.
14. Deng L, Yu D. Deep learning: methods and applications. Foundations and Trends® in Signal Processing. 2014;7(3-4):197-387.
15. Bengio Y. Learning deep architectures for AI. Foundations and Trends® in Machine Learning. 2009;2(1):1-127.
16. Sinecen M, Burak K, Yıldız Ö. [Artificial neural network based early warning system for aydin province towards air factors which primarily affect human health]. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji. 2017;5(4):121-31.
17. Ucuzal H, Yaşar Ş, Çolak C. Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface. 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies. 2019 Oct 11-13; Ankara, Turkey: IEEE; 2019.
18. Goodfellow I, Bengio Y, Courville A. Deep learning. ABD: MIT press; 2016.
19. Jin H, Song Q, Hu X. Auto-keras: An efficient neural architecture search system. Paper presented at: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2002 Aug 3-7; NY, United States: Association for Computing Machinery; 2019.
20. Gulli A, Pal S. Deep Learning with Keras. Birmingham: Packt Publishing Ltd; 2017.
21. Arevalo J, Cruz-Roa A, González FA. Histopathology image representation for automatic analysis: A state-of-the-art review. Revista Med. 2014;22(2):79- 91.
22. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. Tensorflow: A system for large-scale machine learning. Paper presented at: 12th USENIX Symposium on Operating Systems Design and Implementation OSDI 16'. 2016.
23. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research. 2011;12:2825-30.
24. Bradski G, Kaehler A. Learning OpenCV. 1st ed. ABD: OReilly Media. Inc; 2008.
25. McKinney W. pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing. 2011;14(9):1- 9.
26. Walt Svd, Colbert SC, Varoquaux G. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering. 2011;13(2):22-30.
27. Tosi S. Matplotlib for Python Developers. ABD: Packt Publishing Ltd; 2009.
28. Grinberg M. Flask Web Development: Developing Web Applications With Python. 2nd ed. California: O'Reilly Media, Inc; 2018.
29. Yasar S, Arslan AK, Yologlu S, Colak C. DTROC: Tanı Testleri ve ROC Analizi Yazılımı [Web-tabanlı yazılım]. 2019.
30. Hamilton PW, Bankhead P, Wang Y, Hutchinson R, Kieran D, McArt DG, et al. Digital pathology and image analysis in tissue biomarker research. Methods. 2014;70(1):59-73.
31. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
32. Sirinukunwattana K, Ahmed Raza SE, Yee-Wah Tsang, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1196-206.
33. Basha SS, Ghosh S, Babu KK, Dubey SR, Pulabaigari V, Mukherjee S. Rccnet: An efficient convolutional neural network for histological routine colon cancer nuclei classification. 15th International Conference on Control, Automation, Robotics and Vision. 2018 Nov 18-21; Singapore; 2018.
34. Ly T, Sarkar R, Skadron K, Acton ST. Classifying images in a histopathological dataset using the cumulative distribution transform on an automata architecture. 2017 IEEE Global Conference on Signal and Information Processing. 2017 Nov 14-16; Montreal, Canada; 2017.

APA	GÜLDOĞAN E, KÜÇÜKAKÇALI Z, Ucuzal H, ÇOLAK C (2021). Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. , 147 - 159. 10.5336/biostatic.2021-82416
Chicago	GÜLDOĞAN Emek,KÜÇÜKAKÇALI ZEYNEP,Ucuzal Hasan,ÇOLAK Cemil Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. (2021): 147 - 159. 10.5336/biostatic.2021-82416
MLA	GÜLDOĞAN Emek,KÜÇÜKAKÇALI ZEYNEP,Ucuzal Hasan,ÇOLAK Cemil Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. , 2021, ss.147 - 159. 10.5336/biostatic.2021-82416
AMA	GÜLDOĞAN E,KÜÇÜKAKÇALI Z,Ucuzal H,ÇOLAK C Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. . 2021; 147 - 159. 10.5336/biostatic.2021-82416
Vancouver	GÜLDOĞAN E,KÜÇÜKAKÇALI Z,Ucuzal H,ÇOLAK C Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. . 2021; 147 - 159. 10.5336/biostatic.2021-82416
IEEE	GÜLDOĞAN E,KÜÇÜKAKÇALI Z,Ucuzal H,ÇOLAK C "Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach." , ss.147 - 159, 2021. 10.5336/biostatic.2021-82416
ISNAD	GÜLDOĞAN, Emek vd. "Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach". (2021), 147-159. https://doi.org/10.5336/biostatic.2021-82416

APA	GÜLDOĞAN E, KÜÇÜKAKÇALI Z, Ucuzal H, ÇOLAK C (2021). Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. Türkiye Klinikleri Biyoistatistik Dergisi, 13(2), 147 - 159. 10.5336/biostatic.2021-82416
Chicago	GÜLDOĞAN Emek,KÜÇÜKAKÇALI ZEYNEP,Ucuzal Hasan,ÇOLAK Cemil Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. Türkiye Klinikleri Biyoistatistik Dergisi 13, no.2 (2021): 147 - 159. 10.5336/biostatic.2021-82416
MLA	GÜLDOĞAN Emek,KÜÇÜKAKÇALI ZEYNEP,Ucuzal Hasan,ÇOLAK Cemil Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. Türkiye Klinikleri Biyoistatistik Dergisi, vol.13, no.2, 2021, ss.147 - 159. 10.5336/biostatic.2021-82416
AMA	GÜLDOĞAN E,KÜÇÜKAKÇALI Z,Ucuzal H,ÇOLAK C Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. Türkiye Klinikleri Biyoistatistik Dergisi. 2021; 13(2): 147 - 159. 10.5336/biostatic.2021-82416
Vancouver	GÜLDOĞAN E,KÜÇÜKAKÇALI Z,Ucuzal H,ÇOLAK C Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach. Türkiye Klinikleri Biyoistatistik Dergisi. 2021; 13(2): 147 - 159. 10.5336/biostatic.2021-82416
IEEE	GÜLDOĞAN E,KÜÇÜKAKÇALI Z,Ucuzal H,ÇOLAK C "Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach." Türkiye Klinikleri Biyoistatistik Dergisi, 13, ss.147 - 159, 2021. 10.5336/biostatic.2021-82416
ISNAD	GÜLDOĞAN, Emek vd. "Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach". Türkiye Klinikleri Biyoistatistik Dergisi 13/2 (2021), 147-159. https://doi.org/10.5336/biostatic.2021-82416