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Segmentation-based Retinal Image Analysis
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context. Diabetic retinopathy is the most common cause of new cases of legal blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus image is an important basis for judging these retinal diseases. With the development of technology, computer-aided diagnosis is widely used.

Objectives. The thesis is to investigate whether there exist specific regions that could assist in better prediction of the retinopathy disease, it means to find the best region in fundus image that works the best in retinopathy classification with the use of computer vision and machine learning techniques.

Methods. An experiment method was used as research methods. With image segmentation techniques, the fundus image is divided into regions to obtain the optic disc dataset, blood vessel dataset, and other regions (regions other than blood vessel and optic disk) dataset. These datasets and original fundus image dataset were tested on Random Forest (RF), Support Vector Machines (SVM) and Convolutional Neural Network (CNN) models, respectively.

Results. It is found that the results on different models are inconsistent. As compared to the original fundus image, the blood vessel region exhibits the best performance on SVM model, the other regions perform best on RF model, while the original fundus image has higher prediction accuracy on CNN model. Conclusions. The other regions dataset has more predictive power than original fundus image dataset on RF and SVM models. On CNN model, extracting features from the fundus image does not significantly improve predictive performance as compared to the entire fundus image.

Place, publisher, year, edition, pages
2019. , p. 35
Keywords [en]
Retinal Image, Machine Learning, Image Segmentation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18524OAI: oai:DiVA.org:bth-18524DiVA, id: diva2:1341101
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVACS Master of Science Programme in Computer Science
Supervisors
Examiners
Available from: 2019-08-08 Created: 2019-08-07 Last updated: 2019-08-08Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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Output format
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