Pratt, H
(2019)
Deep learning for diabetic retinopathy diagnosis & analysis.
PhD thesis, University of Liverpool.
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Abstract
The diagnosis of diabetic retinopathy (DR) during large-scale diabetic screening is important to prevent sight loss in a significant proportion of the working population. The early detection of disease and quantification of disease progression is vital in order to prevent future loss of vision. Diagnosis of DR is performed through medical image analysis. After the success of deep learning in other real-world applications, deep learning is also providing solutions with good accuracy for medical image analysis and is seen as a key method for future applications in the health sector. Current DR image analysis methods offer some automation of the feature extraction process for features of DR but do not utilise the benefits of deep learning. The application of initial deep learning methods to the diagnosis of DR presented in this thesis show promising initial results on referable DR diagnosis. The extension of these initial methods to more complex deep learning models and correlating multiple eye information shows that deep learning can obtain a state-of-the-art classification for the referral of DR. The classification of DR into more granular diagnosis also achieves reasonable accuracy. This thesis also presents ... (continues)
| Item Type: | Thesis (PhD) |
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| Divisions: | Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Jul 2019 11:17 |
| Last Modified: | 07 Feb 2025 04:27 |
| DOI: | 10.17638/03046567 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3046567 |
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