Detecting Eye Diseases and Intraocular Lesions from Fundus Images Using Deep Learning Approaches

Shakeri Hoosein Abad, Esmaeil

Detecting Eye Diseases and Intraocular Lesions from Fundus Images Using Deep Learning Approaches

dc.contributor.advisor	Far, Behrouz
dc.contributor.advisor	Crump, Trafford
dc.contributor.author	Shakeri Hoosein Abad, Esmaeil
dc.contributor.committeemember	Mohammed, Emad
dc.contributor.committeemember	Kim, Kangsoo
dc.date	2024-02
dc.date.accessioned	2024-01-03T16:40:02Z
dc.date.available	2024-01-03T16:40:02Z
dc.date.issued	2023-12-20
dc.description.abstract	In this study, the focus begins with addressing the critical issue of diabetic retinopathy (DR) detection, a leading cause of blindness globally, by using a combination of SHapley Additive exPlanations (SHAP) analysis and transfer learning ResNet50 model. Achieving impressive accuracy rates of 97% for binary and 81% for multi-class DR classification, the study demonstrates the potential of SHAP analysis to enhance interpretability and contextual understanding of prediction outcomes. Shifting the study to uveal melanoma (UM), an intraocular cancer with significant risks, the research used similar methodologies to predict UM, achieving a high binary classification accuracy of 82.5% in InceptionV3 model. The application of SHAP analysis once again highlights its value in shedding light on prediction rationales and improving result comprehension. The study further extends into the use of four distinct convolutional neural network (CNN)-based architectures for UM detection, emphasizing the manual collection and preprocessing of 854 RGB fundus images. Through transfer learning, DenseNet169 appears as the most accurate model, achieving 89% accuracy in binary classification of choroidal nevus (CN). Essentially, SHAP analysis continues to play an essential role in enhancing interpretability, offering detailed insights into the significant image regions influencing CN predictions. In conclusion, this study emphasises the power of combining deep transfer learning CNN-based models, and SHAP analysis to not only achieve robust predictive performance but also to address the critical challenge of interpretability in deep learning models, contributing significantly to the fields of medical image analysis and diagnostic decision-making.
dc.identifier.citation	Shakeri Hoosein Abad, E. (2023). Detecting eye diseases and intraocular lesions from fundus images using deep learning approaches (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117838
dc.language.iso	en
dc.publisher.faculty	Schulich School of Engineering
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Diabetic Retinopathy
dc.subject	SHapley Additive ex-Planations (SHAP) analysis
dc.subject	Deep learning
dc.subject	Uveal melanoma
dc.subject	Choridal nevus
dc.subject	Classification
dc.subject	Detection
dc.subject.classification	Artificial Intelligence
dc.title	Detecting Eye Diseases and Intraocular Lesions from Fundus Images Using Deep Learning Approaches
dc.type	master thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.