Supervisor
Dr Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
Retinal diseases such as Choroidal Neovascularization, Diabetic Macular Edema and Drusen can affect vision if they are not detected and treated early. Optical Coherence Tomography images provide detailed cross sectional views of the retina, making them useful for identifying retinal abnormalities. This project investigates the use of deep learning for classifying retinal images from the OCT2017 dataset into four categories: CNV, DME, DRUSEN, and NORMAL. The project follows the CRISP-DM methodology, covering data understanding, data preparation, modelling, evaluation and deployment. During data preparation, duplicate images were removed, image dimensions were standardised and class imbalance was addressed using class weights during model training.
Two deep learning approaches were developed and compared a Custom Convolutional Neural Network trained from scratch and a ResNet50 model using transfer learning. Hyperparameter tuning was performed for the Custom CNN using Keras Tuner Hyperband and both models were trained and evaluated using till 30 epochs. The Custom CNN was selected as the final model because it achieved the best overall balance between validation performance and test performance, ResNet50 also performed well, but it was slightly lower than the Custom CNN. Finally, the selected model was deployed using Gradio in Google Colab, allowing users to upload an OCT image and receive a predicted class, confidence score and Grad-CAM heatmap to visualise the model’s focus area.
Date of Award
2026
Full Publication Date
2026
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Afzal, A.
(2026) Deep Learning Based Classification of Retinal OCT Images Using Custom CNN and ResNet50 CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.143