YOLOV9 İLE KAN HÜCRELERİNİN OTOMATİK TANIMLANMASI: OPTİMİZASYON VE ÖĞRENME ORANI ETKİLERİ

Year 2024, , 125 - 135, 30.04.2024

Zehra Yücel , Dilber Çetintaş

https://doi.org/10.54365/adyumbd.1388891

Abstract

Kanda yer alan kan hücrelerinin mikroskobik incelenmesi zaman alıcı, pahalı ve hataya açık bir iştir. Bu çalışmanın amacı, kan hücresi görüntülerini kullanarak kan hücresi tiplerinin sınıflandırılması için YOLO mimarisini kullanan otomatik bir sistem geliştirmektir. Çalışmada kullanılan BCDD veri seti, 364 kan hücresi görüntüsü ve 4888 etiketli görüntüden oluşmaktadır. Açık kaynaklı BCCD veri seti, kırmızı kan hücrelerini (RBC'ler), beyaz kan hücrelerini (WBC'ler) ve trombositleri içerir. Geliştirilen senaryoda YOLOv9 mimarisi, farklı optimizasyon algoritmaları, öğrenme oranları kullanılarak hiperparametrelerin tanımlama sürecindeki etkisi gözlemlendi. Tanımlama sonuçlarını karşılaştırırken en iyi sonuca, 0,001 öğrenme oranıyla ADAMW optimizasyon algoritması kullanılarak ulaşıldı. Genel olarak kan hücresi tiplerinin sınıflandırılmasında WBC tanımlamasında 1,0'a yakın sonuç elde edildi. Daha sonra RBC tanımlaması yaklaşık olarak 0,93 doğrulukla elde edilirken trombositler 0,96 doğrulukla tanımlandı. Bu sonuçlar, önerilen sistemin kan hücresi tanımlamasının manuel sürecini otomatikleştirmeye yönelik etkili bir araç olarak kullanılabileceğini göstermektedir.

Keywords

Kan Hücresi Tespiti, Derin Öğrenme, Yolov9, Optimizasyon, Öğrenme Oranı

AUTOMATIC IDENTIFICATION OF BLOOD CELLS WITH YOLOV9: OPTIMIZATION AND LEARNING RATE EFFECTS

Abstract

Microscopic examination of blood cells in blood is time-consuming, expensive and error-prone. The aim of this study is to develop an automatic system using YOLO architecture for classification of blood cell types using blood cell images. The BCDD dataset used in the study consists of 364 blood cell images and 4888 labeled images. The open-source BCCD dataset includes red blood cells (RBCs), white blood cells (WBCs), and platelets. In the developed scenario, the YOLO v9 architecture, different optimization algorithms, and learning rates were used to observe the effect of hyperparameters in the parameter definition process. When comparing the identification results, the best result was achieved using the ADAMW optimization algorithm with a learning rate of 0.001. Overall, a result close to 1.0 was obtained in the WBC classification for blood cell types. Subsequently, the RBC identification was achieved with an accuracy of approximately 0.93, while platelets were identified with an accuracy of 0.96. These results indicate that the proposed system could be used as an effective tool for automating the manual process of blood cell identification.

Keywords

Blood Cell Detection, Deep Learning, Yolov9, Optimization, Learning Rate

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