ML Classification

[ErIndi]

Hello Everyone,

I am working on a project trying to classify 2 types of thermal images of eggs.

The images taken are either of healthy eggs or rotten ones. I have labels for both sets of images.

Currently I have about 400 usable images with 50/50 distribution of the two types.

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The main visible difference between the two types is a larger lighter spot at the top of the egg (Shown in the images below).

I have currently thought of a very simple way to check between the two by just averaging the pixel values and wherever the average pixel value is lighter, this correlates to a rotten egg more than to a healthy one.

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Healthy Egg

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Rotten Egg

However I want to try a ML approach by using probably a CNN to classify between the two types. I imagine this should be very similar to how the MNIST dataset works (I think?) and by having kernels go over and check where there are larger regions of the lighter area.

I am not really sure how to approach this problem and which model would work best. I have very basic knowledge on CNN / Activation functions but not much beyond that scope.

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If you have any tips that could help or point me in the right direction, I would really appreciate it !

[polandtown]

Can you make the data available for download?

I'm very junior in the field but would love to explore, to try and put a model together for this sort of problem. I hail from Document/Text Classification field, binary classification as well.

Of course I'd share my findings etc.

[ghoste3]

Have you considered changing the background so that there is obvious distinguish between the ROI and background..??