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UPDATE:

I have created the dataset and run the model here: https://github.com/woodytwoshoes/Eyetrain.git

I'm a medical student trying to produce a machine learning model which recognizes a particular feature of the eye: the Pupil-Limbus Ratio. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387813/

The images I have saved contain the PLR as calculated by an algorithm. GoodPLR_[pupil-limbus ratio is here]_[random number is here]

https://drive.google.com/open?id=1J1JRFq_l8aFEshFQVrmDhbDLqK7B24c7

The dataset is small, and I understand this will significantly limit the model, but a larger dataset will arrive in a month's time.

Is it correct that I must use a least-squares regression? I know that a classification model is not appropriate.

Perhaps using Jupyter notebook, is there a simple way to set up a fast.ai model to predict PLR based on this dataset?

Thank you.

PLR is useful in head trauma, neurological conditions, and psychiatry.

I used a self-designed algorithm to quickly create a dataset of images with PLR, but is has a high failure rate, and a high error rate. Erroneous PLRs are not contained in the dataset.

I am currently on lesson 1 of fast.ai https://drive.google.com/open?id=1Uzulez6NQRxXoi_iJyyOQaV3bb1nWIcR

I am hoping for a very rough model with a high error rate due to small dataset. But it is something I can improve later as more data arrives.

Woodsworthy Wordsworth
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1 Answers1

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The suitable way would be to use a Conv-Net using transfer learning. Fast-Ai provides for transfer learning in the first lesson itself..they use resnet30. Follow detailed notes of the lectures and the notebooks..your exact problem is not very clear though..do mention in detail

Shreyas Nair
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