CNN regression hyperparameter optimizer

CNN regression tool built to identify optimal network parameters using cross validation, includes image augmentation and random oversampling

Deep learning convolutional neural network regression

With network parameter gridsearch, input normalization and geometric image augmenation:

Network is designed to learn to predict a numerical value from images

Provided example input (X)data is 3 channel (R2*, QSM, GRE mag) MR image slices of ex vivo stroke blood clots

Provided example output (Y)data is RBC content of clots determined through histological analysis

Built to iterate cross-validation experiments over a set of specified network parameters (gridsearch)

Can customize which parameters to iterate over by changing the for loops

Network settings which produce the highest accuracy will be used to form predictions on a separate test set

Network can normalize training data input based on distribution of the output variable using random oversampling (ROS)

Network can apply random geometric augmentation operations to training data

And duplicate training dataset to increase size, prior to augmenation (duplication factor)

Handles input predictor (x) data as [X, Y, nchannel, nslice] where nchannel = 1 or 3

Handles input prediction (y) data as [y(1):y(nslice)]