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Analysing retinal fundus images with deep learning models
Thesis (PhD)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867613880482529280 |
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| access_status_str | Open Access |
| author | Ofosu Mensah, Samuel |
| author2 | Bah, Bubacarr |
| author_browse | Bah, Bubacarr Ofosu Mensah, Samuel |
| author_facet | Bah, Bubacarr Ofosu Mensah, Samuel |
| author_sort | Ofosu Mensah, Samuel |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/128788 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA en_ZA |
| last_indexed | 2026-06-10T12:43:10.408Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/128788 Analysing retinal fundus images with deep learning models Ofosu Mensah, Samuel Bah, Bubacarr Brink, Willie Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Applied Mathematics Division. Deep learning (Machine learning) Computer vision Convolutional neural networks Convolutions (Mathematics) Neural networks (Computer science) Diabetic retinopathy Thesis (PhD)--Stellenbosch University, 2023. ENGLISH ABSTRACT: Convolutional neural networks (CNNs) have successfully been used to classify diabetic retinopathy but they do not provide immediate explanations for their decisions. Explainability is relevant, especially for clinicians. To make results explainable, we use a post-attention technique called gradient-weighted class activation mapping (Grad- CAM) on the penultimate layer of deep learning models to produce localisation maps on retinal fundus images after using them to classify diabetic retinopathy. Moreover, the models were initialised using pre-trained weights obtained from training models on the ImageNet dataset. The results of this are fewer training epochs and improved performance. Next, we predict cardiovascular risk factors (CVFs) using retinal fundus images. In detail, we use a multi-task learning (MTL) model since there are several CVFs. The impact of using an MTL model is the advantage of simultaneously training for and predicting several CVFs rather than doing so individually. Also, we investigate the performance of the fundus cameras used to capture the retinal fundus images. We notice a superior performance of the desktop fundus cameras to the handheld fundus camera. Finally, we propose a hybrid model that fuses convolutions and Transformer encoders. This is done to harness the benefits of convolutions and Transformer encoders. We compare the performance of the proposed model with other attention-based models and observe on-par performance. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Doctorate 2023-11-27T05:31:13Z 2024-01-08T11:11:35Z 2023-11-27T05:31:13Z 2024-01-08T11:11:35Z 2023-12 Thesis https://scholar.sun.ac.za/handle/10019.1/128788 en_ZA en_ZA Stellenbosch University xvii, 117 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Deep learning (Machine learning) Computer vision Convolutional neural networks Convolutions (Mathematics) Neural networks (Computer science) Diabetic retinopathy Ofosu Mensah, Samuel Analysing retinal fundus images with deep learning models |
| title | Analysing retinal fundus images with deep learning models |
| title_full | Analysing retinal fundus images with deep learning models |
| title_fullStr | Analysing retinal fundus images with deep learning models |
| title_full_unstemmed | Analysing retinal fundus images with deep learning models |
| title_short | Analysing retinal fundus images with deep learning models |
| title_sort | analysing retinal fundus images with deep learning models |
| topic | Deep learning (Machine learning) Computer vision Convolutional neural networks Convolutions (Mathematics) Neural networks (Computer science) Diabetic retinopathy |
| url | https://scholar.sun.ac.za/handle/10019.1/128788 |
| work_keys_str_mv | AT ofosumensahsamuel analysingretinalfundusimageswithdeeplearningmodels |