Eye Ailment Detection Using Deep Learning
Published in Undergraduate Research Project, 2022
This research project introduced a state-of-the-art method using deep learning to detect different eye pathologies. The work focused on implementing and evaluating multiple CNN architectures for medical image classification, achieving high accuracy in identifying various eye diseases from retinal images.
Project Overview
Objective: Develop an automated system for detecting eye diseases using deep learning techniques
Methodology:
- Implemented convolutional neural networks for medical image analysis
- Evaluated multiple deep learning architectures
- Performed transfer learning from pre-trained models
- Optimized for high accuracy and clinical applicability
Key Contributions:
- State-of-the-art performance in eye pathology detection
- Comprehensive evaluation of different CNN architectures
- Potential application in clinical settings for early disease detection
Supervisor: Dr. Hamidreza Shayegh
Institution: Computer Vision Research Group, Shahid Rajaee Teacher Training University
Duration: Fall 2021 - Spring 2022
Recommended citation: Gholizadeh, M.S. (2022). "Eye Ailment Detection Using Deep Learning." Shahid Rajaee Teacher Training University.
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