Using Neural Networks for Diabetic Retinopathy Detection in Eye Images
Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital color fundus photographs of the retina. By the time human readers submit their reviews, often a day or two later, the delayed results lead to lost follow up, miscommunication, and delayed treatment (from http://www.kaggle.com/c/diabetic-retinopathy-detection).
Thus, we will try to develop an automated method for detecting DR in eye images using machine learning techniques in particular we will try Neural Networks approach. First, we will start with implementing simple Softmax classifier, later substituting it with three-layer artificial neural network, ultimate goal is to try to build a convolutional neural network for image classification. In this project we will follow up the Convolutional Neural Networks Course, which is an open online course from Stanford (http://cs231n.github.io/).
- Matrix/vector arithmetics
- Introduction to machine learning:
- Classification/regression (hands on some basic classifiers)
- Cost function optimization (gradient descent)
- Cross validation techniques (leave-one-out cross validation, ten-fold cross validation)
- Parameters tuning (grid search/random search)
- Model evaluation
- Programming language: Python
- Introduction to Neural Networks
Image classification, neural networks, cost function optimization, parameters tuning, comparing models
- Introducation to Python
Mr. Dmytro Fishman,
PhD student, junior researcher at the University of Tartu.