Segmentation of Microscopic Blood Cell Images with Current Deep Learning Architectures

Öz

Blood cells are the basic components of blood, and they play an important role in the healthy functioning of the human body. The shape, number, size, and other characteristics of blood cells are dependent on various factors, and changes in these properties can be associated with many diseases. Therefore, the detection, classification, and segmentation of blood cells have become a very important issue in the field of health. With the high performance effect of deep learning architectures on medical images, the number of automatic diagnosis systems on these blood cells has increased. In this article, cell segmentation was performed on microscopic blood cell images using DeepLabv3+, U-Net, and FCN architectures. The best accuracy result was the accuracy with a score of 0.9575 in the DeepLabv3+ architecture. The experimental results support the robustness of the proposed method.

Anahtar Kelimeler

• Cell
• Segmentation
• DeepLabV3+

Mikroskobik Kan Hücre Görüntülerinin Güncel Derin Öğrenme Mimarileri ile Bölütlemesi

Öz

Kan hücreleri, kanın temel bileşenleridir. Bu bileşenler insan vücudunun sağlıklı bir şekilde çalışmasında önemli rol oynarlar. Kan hücrelerinin şekli, sayısı, boyutu ve diğer özellikleri çeşitli faktörlere bağlıdır. Bu özelliklerin değişimleri birçok hastalıkla ilişkilendirilebilmektedir. Bu nedenle, kan hücrelerinin tespit edilmesi, sınıflandırılması ve bölütlenmesi sağlık alanında çok önemli bir konu haline gelmiştir. Derin öğrenme mimarilerinin medikal görüntüler üzerinde göstermiş olduğu yüksek performans etkisiyle bu kan hücreleri üzerinde otomatik tanı sistemlerinin sayısı artmıştır. Bu makalede, DeepLabv3+, U-Net ve FCN mimarileri ile mikroskobik kan hücresi görüntüleri üzerinde hücre bölütlemesi yapılmıştır. En iyi doğruluk sonucuna 0.9575 ile DeepLabv3+ mimarisinde ulaşılmıştır. Deneysel sonuçlar, önerilen yöntemin sağlamlığını destekler niteliktedir.

Anahtar Kelimeler

• Hücre
• Bölütleme
• DeepLabV3+
• U-Net
• FCN

Kaynakça

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