Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
Detection of Different Tissue Types of Colorectal Cancer Based on Histological Images Using Deep Learning Approach
Derin Öğrenme Yaklaşımı Kullanılarak Histolojik Görüntülere Dayalı Kolorektal Kanserin Farklı Doku Tiplerinin Saptanması
Emek GÜLDOĞANa, Zeynep KÜÇÜKAKÇALIa, Hasan UCUZALa, Cemil ÇOLAKa
aDepartment of Biostatistics and Medical Informatics, İnönü University Faculty of Medicine, Malatya, TURKEY
Turkiye Klinikleri J Biostat. 2021;13(2):147-59
doi: 10.5336/biostatic.2021-82416
Article Language: EN
Full Text
ABSTRACT
 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.

Keywords: Multiple classification; tissue types; colorectal cancer; deep learning architecture; Keras/AutoKeras
ÖZET
 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 Kelimeler: Çoklu sınıflandırma; doku tipleri; kolorektal kanser; derin öğrenme mimarisi; Keras/AutoKeras
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