GitHub - WahabBasa/Detect-Retina-Damage-Using-Transfer-Learning: Transfer learning will be used to train a deep learning model on a small dataset of less than 1,000 retinal OCT images. This approach addresses the challenge of limited data availability in medical imaging, particularly for retinal optical coherence tomography scans, which are widely used but time-consuming to analyze.

Project Overview

Retinal optical coherence tomography (OCT) is an imaging technique used to capture high-resolution cross-sections of the retinas of living patients. Approximately 30 million OCT scans are performed each year, and the analysis and interpretation of these images takes up a significant amount of time (Swanson and Fujimoto, 2017).

Due to the difficulty in obtaining large datasets of medical images, we aim to use transfer learning to train a deep learning model using only a small dataset consisting of less than 1,000 retinal OCT images.

Requirements

Learn Python language
Use popular ML and DL frameworks such as:
- SciKit-Learn
- PyTorch
- TensorFlow (backend engine)
Implement Flask (web application framework) for the front-end interface