Improving Classification and Segmentation of Choroidal Lesions by Addressing Data Limitations with Patch-Based Approaches

Biglarbeiki, Mehregan

Improving Classification and Segmentation of Choroidal Lesions by Addressing Data Limitations with Patch-Based Approaches

dc.contributor.advisor	Far, Behrouz
dc.contributor.advisor	Crump, Trafford
dc.contributor.author	Biglarbeiki, Mehregan
dc.contributor.committeemember	Messier, Geoffrey
dc.contributor.committeemember	Bento, Mariana
dc.date	2024-11
dc.date.accessioned	2024-09-25T22:24:47Z
dc.date.available	2024-09-25T22:24:47Z
dc.date.issued	2024-09-19
dc.description.abstract	Choroidal nevi are benign ocular lesions that can progress to malignant forms like choroidal melanoma. Recent advancements in Deep Learning (DL) have shown potential in detecting ocular diseases, automating eye reviews, and facilitating timely treatment. However, these models require extensive labelled data, which is challenging to acquire due to the associated labeling costs. This limitation affects the optimal performance of DL models, leading to underexplored applications in this domain, with only a few studies available. This thesis presents three studies aimed at overcoming data limitations and enhancing model performance for the classification and segmentation of melanocytic choroidal tumors in fundus images. The first study involved binary classification of choroidal nevi and healthy subjects, using fundus images from the Alberta Ocular Brachytherapy dataset. Pre-trained models—ResNet50, DenseNet121, EfficientNetB7, and YOLOv8n—were evaluated. Results indicated that training on image patches rather than full-size images, combined with data augmentation to address noisy images and low-contrast lesions, resulted in the YOLOv8n model achieving the highest accuracy of 92.61%. The second study aimed to segment choroidal nevi lesions from fundus images. U-Net and YOLOv8n segmentation models were trained on the Alberta Ocular Brachytherapy dataset and validated on the Wills Eye Hospital dataset. The YOLOv8n model achieved Dice Coefficient scores of 0.833 and 0.764 for the Alberta and Wills datasets, respectively, when trained on both full-size images and image patches, along with the proposed post-processing methods. In the final study, a YOLOv8n model classified choroidal melanoma and nevi lesions from montage fundus images. The model’s performance was evaluated under two pre-training scenarios. The model pre-trained on ImageNet and fine-tuned on our dataset achieved an accuracy of 92.25%, outperforming the model pre-trained on ImageNet and fine-tuned on the Kaggle EyePACs dataset before the final fine-tuning on our dataset. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to enhance model interpretability, providing ocular oncologists with insights into the model’s predictions. Overall, these studies emphasize the significance of DL models in automating the detection of melanocytic choroidal lesions and improving their classification and segmentation performance.
dc.identifier.citation	Biglarbeiki, M. (2024). Improving classification and segmentation of choroidal lesions by addressing data limitations with patch-based approaches (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/119908
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	Deep Learning
dc.subject	Medical Image Analysis
dc.subject	Classification
dc.subject	Segmentation
dc.subject	Fundus Imaging
dc.subject	Choroidal Nevi
dc.subject	Choroidal Melanoma
dc.subject.classification	Computer Science
dc.subject.classification	Engineering--Electronics and Electrical
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Ophthalmology
dc.title	Improving Classification and Segmentation of Choroidal Lesions by Addressing Data Limitations with Patch-Based 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.