Sıkma-Uyarma Artık Ağı kullanılarak Beyaz Kan Hücrelerinin Sınıflandırılması

Year 2023, Volume: 16 Issue: 3, 189 - 205, 31.07.2023

Hüseyin Fırat

https://doi.org/10.17671/gazibtd.1255477

Cited By: 7

Abstract

Beyaz kan hücreleri, vücudun parazitler, bakteriler, virüsler gibi mikroorganizmalara karşı korunmasında etkin rol oynayan bağışıklık sisteminin önemli bir bileşenidir. Beyaz kan hücrelerinin yapısal özellikleri, alt türlerinin şekilleri ve sayıları insan sağlığı hakkında önemli bilgiler verebilmektedir. Hastalık teşhisinde doğru beyaz kan hücre tespiti klinik olarak oldukça önemlidir. Bu yüzden, doğru beyaz kan hücre sınıflandırma yöntemi kritik öneme sahiptir. Bu çalışmada, beyaz kan hücre sınıflandırması için Evrişimsel sinir ağı (ESA) tabanlı bir yöntem önerilmiştir. Önerilen yöntem sıkma-uyarma ağı ile artık ağ mimarisinin birleşiminden oluşan hibrit bir yöntemdir. Derin ağ mimarilerinde katman sayısı arttıkça oluşabilecek problemler artık ağ ile çözülebilmektedir. Sıkma-uyarma (SU) bloğunun artık ağ ile birlikte kullanımı, toplam parametre sayısını minimum düzeyde arttırırken sınıflandırma doğruluğunu arttırmakatdır. Aynı zamanda, SU bloğunun artık ağ ile birleştirilmesi geleneksel artık ağların performansını da arttırmaktadır. Önerilen yöntemin performansını test etmek için Kaggle veritabanından alınan BCCD veriseti kullanılmıştır. Uygulamalar sonucunda ortalama %99,92 doğruluk, %99,85 kesinlik, duyarlılık ve F1-skoru elde edilmiştir. Bu sonuçlar, literatürden BCCD verisetini kullanan son yıllardaki çalışmalarda yer alan ESA yöntemlerinin elde ettiği sonuçlarla karşılaştırıldı ve önerilen yöntemin daha az eğitilebilir parametre ile daha iyi sonuçlar verdiği görülmüştür.

Keywords

Beyaz kan hücresi, sınıflandırma, sıkma-uyarma artık ağı, evrişimsel sinir ağı

Classification of White Blood Cells using the Squeeze-Excitation Residual Network

Abstract

White blood cells (WBCs) are an important component of the immune system that plays an active role in protecting the body against microorganisms such as parasites, bacteria and viruses. The structural features of WBCs, the shapes and numbers of their subtypes can provide important information about human health. Accurate WBC detection is clinically very important in the diagnosis of the disease. Accordingly, an accurate WBC classification method is of critical importance. In this study, a CNN-based method for WBC classification is proposed. The proposed method is a hybrid method consisting of a combination of squeeze-excitation (SE) network and residual network (ResNet) architecture. The problems that may occur as the number of layers increase in deep network architectures can be solved with ResNet. The use of the SE block with ResNet increases the classification accuracy while minimally increasing the total number of parameters. At the same time, combining the SE block with the ResNet improves the performance of traditional ResNets. The BCCD dataset from the Kaggle database was used to test the performance of the proposed method. As a result of the applications, an average of 99.92% accuracy, 99.85% precision, recall and F1-score were obtained. These results were compared with the results obtained by the CNN methods in recent studies using the BCCD dataset from the literature, and it was seen that the proposed method gave better results with less trainable parameters.

Keywords

White blood cell, classification, squeeze-excitation residual network, convolutional neural network

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