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I'm working in Diabetic Retinopathy Detection problem where I've been given Retina images [image1] with score labels. My job is to build a classification model that can detect and score retinopathy given unlabeled retina images.

First step that I'm currently working on is extracting features from these images and build input vector that I'll use as an input for my classification algorithm. I've basic knowledge in image processing and I've tried to crop my images to edges [Image2], turn it to Gray scale and get its histogram as an input vector but it seems that I still have a large representation for an image. In addition to that I may have lost some essential features that was encoded into the RGB image.

Image1:

enter image description here

Image2:

enter image description here

Nathan Tuggy
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Assem
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    You might find some useful advice here: https://www.kaggle.com/c/diabetic-retinopathy-detection/forums - however, the request for resources and generic advice does not make a good question on Stack Overflow. If you could focus on one specific coding problem that you have, then SO might be able to help. – Neil Slater May 09 '15 at 20:10
  • Thanks, appreciate your attempt to help – Assem May 09 '15 at 21:24
  • First thing is to establish which are the visual clues that can tell healthy from sick retina. Trying features randomly does not seem the best approach. If necessary, ask the advice of a specialist. (By the way, an histogram is a very poor feature, certainly irrelevant here.) –  Aug 03 '22 at 19:42

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pre-processing medical images is not a trivial task, for the performance improvement of diabetic retinopathy you need to highlight the blood vessels, there are several pre-processing suitable for this, I am sending a link that may be useful

https://github.com/tfs4/IDRID_hierarchical_combination/blob/master/preprocess.py
Thiago Rainmaker
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