LÖKOSİT TESPİTİ İÇİN BEYAZ KAN HÜCRELERİNİN ESA KULLANILARAK SINIFLANDIRILMASI

Yıl 2022, Cilt: 9 Sayı: 17, 333 - 344, 31.08.2022

Mucella Özbay Karakuş , Erdal Özbay

https://doi.org/10.54365/adyumbd.1069856

Cited By: 1

Öz

Beyaz kan hücreleri, insanların bağışıklık sisteminin en önemli yapısı olup, kan ve lenf dokularında kemik iliği tarafından üretilmektedir. Bu hücreler insan vücudunu hastalık ve yabancı organizmalara karşı koruyan savunma mekanizmalarıdır ve kandaki oranı düştüğünde Lökopeni ile karşılaşılabilir. Bu hücrelerin insan vücudundaki oranının belirlenmesi ve hastalığın tespit ve tedavisi için yoğun emek harcaması gerekmektedir. Bu çalışmada, derin öğrenme modellerini kullanarak beyaz kan hücreleri sınıflandırma performansının iyileştirilmesi amaçlanmıştır. Sınıflandırma işlemini daha verimli gerçekleştirmek için evrişimli sinir ağı modelleri kullanılmıştır. Beyaz kan hücresi çeşitleri olan eozinofil, lenfosit, monosit ve nötrofil arasında ayrım yapmak için Densenet201, ResNet50 ve Alexnet birleştirilmiştir. Elde edilen özellik haritalarının sınıflandırılması için K-En yakın komşuluk, Destek Vektör Makinesi ve Naïve Bayes olmak üzere üç farklı makine öğrenmesi sınıflandırıcısı kullanılmıştır. Derin Öğrenme (DÖ) ile eğitilen Kaggle veri kümesi görüntülerine CLAHE ve Gauss filtreleri uygulanarak bu görüntüler üç ESA ağı ile yeniden sınıflandırılmıştır. Bu filtreler uygulandıktan sonra elde edilen sonuçların, orijinal verilerle elde edilen sınıflandırma sonuçlardan daha yüksek olduğu ortaya konmuştur.

Anahtar Kelimeler

Beyaz kan hücreleri, Derin öğrenme, ESA, Görüntü işleme, Sınıflandırma

CLASSIFICATION OF WHITE BLOOD CELLS USING CNN FOR THE DETECTION OF LEUCOCYTE

Öz

White blood cells are the most important structure of the human immune system and are produced by the bone marrow in the blood and lymph tissues. These cells are the defense mechanisms that protect the human body against diseases and foreign organisms, and Leukopenia may be encountered when the rate in the blood decreases. Intensive effort is required to determine the ratio of these cells in the human body and to detect and treat the disease. In this study, it is aimed to improve the white blood cell classification performance by using deep learning models. Convolutional neural network models are used to perform the classification process more efficiently. Densenet201, ResNet50, and Alexnet were combined to distinguish between the white blood cell variants, eosinophils, lymphocytes, monocytes, and neutrophils. Three different machine learning classifiers, namely K-Nearest Neighborhood, Support Vector Machine and Naïve Bayes, were used to classify the obtained feature maps. By applying CLAHE and Gaussian filters to Kaggle dataset images trained with Deep Learning (DL), these images were reclassified with three CNN networks. It has been revealed that the results obtained after applying these filters are higher than the classification results obtained with the original data.

Anahtar Kelimeler

White blood cells, Deep learning, CNN, Image processing, Classification

Kaynakça

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