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

Year 2021, Issue: 29, 129 - 137, 01.12.2021

Emre Dandıl , Ali Osman Selvi , Kerim Kürşat Çevik , Mehmet Süleyman Yıldırım , Süleyman Uzun

https://doi.org/10.31590/ejosat.1018309

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

Supporting Institution

Scientific Research Projects Department of Bilecik Seyh Edebali University

Project Number

2019-01.BŞEÜ.25-02

Thanks

This study was supported by Scientific Research Projects Department of Bilecik Seyh Edebali University with the project numbered 2019-01.BŞEÜ.25-02. The team would like to thank Scientific Research Projects Department of Bilecik Seyh Edebali University for their contributions. We also thank to providers of publicly-available datasets.

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

Abstract

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.

Keywords

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

Project Number

2019-01.BŞEÜ.25-02

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