Histopatolojik Görüntüler Kullanarak Göğüs Kanseri Sınıflandırması İçin Özellik Birleştirmeye Dayalı Melez Bir Yöntem

Öz

Günümüzde kadınlar arasında en sık görülen kanser türü meme kanseridir ve akciğer kanserinden sonra oldukça yüksek bir ölüm oranına sahip olarak ikinci sırada yer alır. Geç tespit edilmesi durumunda ise meme kanserinin tedavisi oldukça zor bir duruma gelmektedir. Meme kanserinin tespiti için çeşitli yöntemler bulunmasına karşın, halen yardımcı tespit ve tedavi yöntemlerine olan ihtiyaç duyulmaktadır. Bu çalışmada, histopatolojik görüntüler kullanılarak bazal benzeri meme tümörlerinin gelişimini incelemek ve bazal benzeri meme kanserleri türlerini sınıflandırmak için melez bir sistem önerilmektedir. Yapılan çalışmada, ilk önce tümörlü ve tümörlü olmayan bölgeler arasında doğru sınıflandırmayı destekleyen kullanışlı özelliklerin çıkartılması sağlanmıştır. Daha sonra, elde edilen özelliklerin birleştirilmesi ile veriseti oluşturulmuştur. Çalışmanın son aşamasında ise, kelime çantası (bag of words) ve derin sinir ağları (deep neural networks) modelleri hibrit bir biçimde kullanarak görüntülerin sınıflandırma işlemi gerçekleştirilmiştir. Literatürde bu sınıflandırma için immünohistokimyasal belirteçler kullanılmaktadır, fakat bu belirteçlerin başarımları ise %60 seviyelerinde kalmaktadır. Bu çalışmada, histopatolojik görüntülerden elde edilen özellikler birleştirilerek, önerilen melez sınıflandırıcı ile sistemin sınıflandırma doğruluğu başarımının artırılması sağlanmıştır. Gerçekleştirilen çalışma sonucunda, eğitim kümesi ile %94.5 sınıflandırma doğruluğuna ulaşılırken, test kümesi ile %80.8 sınıflandırma doğruluğu başarılmıştır. Böylece, histopatolojik görüntüler üzerinde bazal benzeri göğüs kanserinin sınıflandırılmasında özellik birleştirmeye dayalı önerilen melez yöntem ile başarılı sonuçlara ulaşıldığı doğrulanmıştır.

Anahtar Kelimeler

• Göğüs kanseri
• Sınıflandırma
• Histopatolojik görüntüler
• Derin sinir ağları
• Kelime çantası
• Özellik birleştirme

A Hybrid Method Based on Feature Fusion for Breast Cancer Classification using Histopathological Images

Abstract

Breast cancer is the most common type of cancer in women today, and it ranks second after lung cancer with a very high mortality rate. If it is detected late, the treatment of breast cancer becomes very difficult. Although there are various methods for the detection of breast cancer, there is still a need for auxiliary diagnosis and treatment methods. In this study, a hybrid method is proposed to investigate the development of basal-like breast tumors and classify basal-like breast cancer types using histopathological images. In the study, firstly, appropriate features that support the accurate classification between tumor and non-tumor regions are extracted from histopathological images. Then the dataset is created by combining the obtained features. In the last stage of the study, the classification of images is carried out by using bag of words (BoW) and deep neural networks (DNN) techniques in a hybrid manner. Generally, immunohistochemical markers are used for this classification, but the performance of these markers remains at 60%. The performance of the classification accuracy of the proposed system is increased with the proposed hybrid classifier based on feature fusion. As a result of the study, 94.5% classification accuracy is achieved on the training set, while 80.8% classification accuracy is succeed on the test set. As a result, it is verified that successful results are achieved in the classification of basal-like breast cancer on histopathological images using the proposed hybrid method based on feature fusion.

Keywords

• Breast cancer
• Classification
• Histopathological images
• Deep neural networks
• Bag of words
• Feature fusion

References

1. Abdel-Zaher, A. M., & Eldeib, A. M. (2016). Breast cancer classification using deep belief networks. Expert Systems with Applications, 46, 139-144.
2. ACS(The American Cancer Society). (2021). How Common Is Breast Cancer? Available: https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html
3. Ali, N. M., Karis, M. S., Abidin, A. F. Z., Bakri, B., Shair, E. F., & Razif, N. R. A. (2015). Traffic sign detection and recognition: Review and analysis. Jurnal Teknologi, 77(20).
4. Andrade, D. V., & de Figueiredo, L. H. (2001). Good approximations for the relative neighbourhood graph. Paper presented at the CCCG.
5. Azar, A. T., & El-Said, S. A. (2013). Probabilistic neural network for breast cancer classification. Neural Computing and Applications, 23(6), 1737-1751.
6. Badowska-Kozakiewicz, A. M., & Budzik, M. P. (2016). Immunohistochemical characteristics of basal-like breast cancer. Contemporary Oncology, 20(6), 436.
7. Badve, S., et al. (2011). Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists. Modern Pathology, 24(2), 157-167.
8. Bloom, H., & Richardson, W. (1957). Histological grading and prognosis in breast cancer: a study of 1409 cases of which 359 have been followed for 15 years. British journal of cancer, 11(3), 359.

9. Budak, Ü., Cömert, Z., Rashid, Z. N., Şengür, A., & Çıbuk, M. (2019). Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Applied Soft Computing, 85, 105765.
10. Chekkoury, A., et al. (2012). Automated malignancy detection in breast histopathological images. Paper presented at the Medical Imaging 2012: Computer-Aided Diagnosis.
11. Clausi, D. A. (2002). An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of remote sensing, 28(1), 45-62.
12. Cruz-Roa, A., et al. (2014). Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Paper presented at the Medical Imaging 2014: Digital Pathology.
13. Çevik, K. K., Dandil, E., Uzun, S., Yildirim, M. S., & Selvi, A. O. (2021). 12 Detection of breast cancer using deep neural networks with transfer learning on histopathological images Artificial Intelligence for Data-Driven Medical Diagnosis (pp. 245-264): De Gruyter.
14. Dai, X., Li, T., Bai, Z., Yang, Y., Liu, X., Zhan, J., & Shi, B. (2015). Breast cancer intrinsic subtype classification, clinical use and future trends. American journal of cancer research, 5(10), 2929.
15. Dandıl, E., & Serin, Z. (2020). Derin Sinir Ağları Kullanarak Histopatolojik Görüntülerde Meme Kanseri Tespiti. Avrupa Bilim ve Teknoloji Dergisi, 451-463.
16. Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems, 91(2), 179-189.
17. Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., & Li, S. (2017). Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific Reports, 7(1), 1-10.
18. Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics(6), 610-621.
19. Ibrahim, F., Thio, T. H. G., Faisal, T., & Neuman, M. (2015). The application of biomedical engineering techniques to the diagnosis and management of tropical diseases: a review. Sensors, 15(3), 6947-6995.
20. Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of pathology informatics, 7.
21. Jones, C. E., Maben, J., Lucas, G., Davies, E. A., Jack, R. H., & Ream, E. (2015). Barriers to early diagnosis of symptomatic breast cancer: a qualitative study of Black African, Black Caribbean and White British women living in the UK. BMJ open, 5(3).
22. Khameneh, F. D., Razavi, S., & Kamasak, M. (2019). Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Computers in biology and medicine, 110, 164-174.
23. Khurd, P., Bahlmann, C., Maday, P., Kamen, A., Gibbs-Strauss, S., Genega, E. M., & Frangioni, J. V. (2010). Computer-aided Gleason grading of prostate cancer histopathological images using texton forests. Paper presented at the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
24. KNIME. Available: https://www.knime.com/
25. Kumar, A., et al. (2020). Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Information Sciences, 508, 405-421.
26. Li, T., Mei, T., Kweon, I.-S., & Hua, X.-S. (2010). Contextual bag-of-words for visual categorization. IEEE Transactions on Circuits and Systems for Video Technology, 21(4), 381-392.
27. Mikhaylov, V., & Bakhshiev, A. (2017). The system for histopathology images analysis of spinal cord slices. Procedia Computer Science, 103(C), 239-243.
28. Mohammed, M. A., Al-Khateeb, B., Rashid, A. N., Ibrahim, D. A., Abd Ghani, M. K., & Mostafa, S. A. (2018). Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images. Computers & Electrical Engineering, 70, 871-882.
29. Nahid, A.-A., Mehrabi, M. A., & Kong, Y. (2018). Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed research international, 2018.
30. Öztürk, Ş., & Akdemir, B. (2018). Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA. Procedia Computer Science, 132, 40-46.
31. Öztürk, Ş., & Akdemir, B. (2019). HIC-net: A deep convolutional neural network model for classification of histopathological breast images. Computers & Electrical Engineering, 76, 299-310.
32. Pudil, P., Novovičová, J., & Kittler, J. (1994). Floating search methods in feature selection. Pattern Recognition Letters, 15(11), 1119-1125.
33. Sertel, O., Lozanski, G., Shana’ah, A., & Gurcan, M. N. (2010). Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation. IEEE transactions on biomedical engineering, 57(10), 2613-2616.
34. Sudharshan, P., Petitjean, C., Spanhol, F., Oliveira, L. E., Heutte, L., & Honeine, P. (2019). Multiple instance learning for histopathological breast cancer image classification. Expert Systems with Applications, 117, 103-111.
35. Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 2021(0), 0-41. doi:https://doi.org/10.3322/caac.21660
36. Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016). Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718.
37. Yan, R., et al. (2020). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 173, 52-60.
38. Zewdie, E. T., Tessema, A. W., & Simegn, G. L. (2021). Classification of breast cancer types, sub-types and grade from histopathological images using deep learning technique. Health and Technology, 1-14.
39. Zhang, Y., Jin, R., & Zhou, Z.-H. (2010). Understanding bag-of-words model: a statistical framework. International Journal of Machine Learning and Cybernetics, 1(1-4), 43-52.