Deep Learning Transformers in Diabetic Retinopathy Detection
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2023-12-11
Department
Major/Subject
Complex Systems
Mcode
SCI3060
Degree programme
Master’s Programme in Life Science Technologies
Language
en
Pages
39
Series
Abstract
Diabetic retinopathy is a progressive ocular disease linked with diabetes mellitus, which can lead to blindness. The constantly high blood glucose levels caused by diabetes damage the vascular system, which can be seen e.g. as damaged retinal blood vessels. Diabetic retinopathy can be detected by inspecting fundus images for any visual changes. This time-consuming process, executed by ophthalmologists, can be automated by creating efficient computer vision algorithms. Convolutional Neural Networks (CNN) have been established as the state-of-the-art architecture in computer vision tasks. However, an architecture based on the self-attention mechanism, called Transformer, has been recently utilized in computer vision with promising results, albeit requiring significantly larger training datasets and computational resources than traditional CNNs. In this thesis, three Transformer architectures, i.e., Swin, Vision Transformer-B/16 and Vision Transformer-B/8 are compared to a state-of-the-art CNN model called EfficientNet-B6, in the detection of the severity of diabetic retinopathy from patients’ fundus images. The results of this thesis indicate that the Transformer architecture has potential to improve upon the CNN in diabetic retinopathy detection. However, the amount of training data and required computational resources are challenges yet to be solved.Description
Supervisor
Marttinen, PekkaThesis advisor
Kaski, KimmoJaskari, Joel
Keywords
deep learning, diabetic retinopathy, transformer, computer vision