Glaucoma classification using a morphological-convolutional neural network trained with extreme learning machine

What is it about?

A glaucoma classification was conducted using a morphological and convolutional neural network (MCNN) that takes advantage of the different types of morphological operations by including them in three independent neural networks joined in a Random Forest at the end.

Featured Image

Photo by Markus Spiske on Unsplash

Photo by Markus Spiske on Unsplash

Why is it important?

Nowadays we find machine learning methods proposed to support diagnosis using extremely deep architectures that require large datasets to be properly trained, however, in the medical field, it is difficult to obtain datasets large enough for this task, which is why we proposed a condensed method that allows to obtain a proper performance using small datasets.