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

Authors : Sümeyra Büşra ŞENGÜL, İlker Ali OZKAN

Pages : 238-247

View : 45 | Download : 61

Publication Date : 2023-08-31

Article Type : Research Paper

Abstract :In order to diagnose and treat diseases, a variety of imaging techniques are used, including X-ray, computed tomography insert ignore into journalissuearticles values(CT);, mammography, ultrasound, and magnetic resonance imaging insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(position, direction, size);, deformation, and texture. The Basic Capsule Network insert ignore into journalissuearticles values(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, Evrişimli Sinir Ağları, Kapsül Ağlar, Medikal Görüntü Analizi