Biyomedikal Görüntülerin Sınıflandırılmasında Temel Kapsül Ağ Mimarisinin Performansının Değerlendirilmesi

Abstract

Hastalıkların teşhis ve tedavisi için röntgen, bilgisayarlı tomografi (BT), mamografi, ultrason ve manyetik rezonans görüntüleme (MRI) gibi çeşitli görüntüleme teknikleri kullanılmaktadır. Bu medikal görüntülerin doğru analiz edilmesi, hastalığın erken teşhisi ve uygun tedavinin uygulanması için gereklidir. Görüntü analizinde ilgili alanın tanımlanması, büyüklüğü, konumu, yönü gibi bilgiler en iyi tedavi yöntemlerinin belirlenmesinde kritik öneme sahiptir. Evrişimli sinir ağı (CNN) mimarisi, tıbbi görüntü analizinde en yaygın kullanılan derin öğrenme mimarilerinden biridir. Ancak CNN görüntü özelliklerini çıkarırken bu özellikler arasındaki ilişkiyi ölçmekte yetersiz kalmakta, poz (konum, yön, boyut), deformasyon, doku gibi özellikleri gizleyememektedir. Temel Kapsül Ağı (CapsNet) Mimarisi, CNN'in bu dezavantajlarını aşmak ve başarısını artırmak için önerilmiştir. Bu çalışmada tıbbi görüntülerden oluşan MedMNIST veri seti topluluğu kullanılmıştır. CapsNet mimarisinin sınıflandırma performansını değerlendirmek için MedMNIST'te yer alan RetinaMNIST, BreastMNIST ve OrganMNIST-A veri kümeleri kullanılmıştır. Capsnet bu veri setleri üzerinde sırasıyla %54, %83 ve %89 doğruluk elde etmiştir. CapsNet' in gelişmiş CNN modellerine yakın sonuçlar aldığı görülmüştür.

Keywords

• CapsNet
• Res-Net
• Evrişimli Sinir Ağları
• Kapsül Ağlar
• Medikal Görüntü Analizi

Performance Evaluation of Basic Capsule Network Architecture in Classification of Biomedical Images

Abstract

In order to diagnose and treat diseases, a variety of imaging techniques are used, including X-ray, computed tomography (CT), mammography, ultrasound, and magnetic resonance imaging (MRI). Correct analysis of these medical images is required for early disease detection and application of the appropriate treatment. In image analysis, the identification of the relevant area, as well as information such as its size, location, and direction, are critical in determining the best treatment methods. The convolutional neural network (CNN) architecture is one of the most widely used deep learning architectures in medical image analysis. However, it was stated that CNN was insufficient to measure the relationship between these features while extracting image features, and it could not hide features such as pose (position, direction, size), deformation, and texture. The Basic Capsule Network (CapsNet) Architecture was proposed to overcome CNN's disadvantage and increase success. In this study, MedMNIST dataset collection consisting of medical images was used. The RetinaMNIST, BreastMNIST, and OrganMNIST-A datasets included in MedMNIST were used to evaluate the classification performance of the CapsNet architecture. Capsnet succeeded in these with accuracy rates of 54%, 83%, and 89%, respectively. CapsNet has been shown to produce comparable results to advanced CNN models.

Keywords

• CapsNet
• Res-Net
• Convolutional Neural Networks
• Capsule Networks
• Medical Image Analysis

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