Conclusion 

This project was a combination of CNN model classification problem (to predict whether the subject has brain tumor or not) & Computer Vision problem (to automate the process of brain cropping from MRI scans). 

The final accuracy is much higher than 50% baseline (random guess). However, it could be increased by larger number of train images or through model hyper parameters tuning.

One of the most important hyper parameters is the batch size, which is the number of images used to train a single forward and backward pass. 

Data augmentation improves the ability of models and models perform as skillful models. 

Dropout is a technique used to prevent a model from overfitting. Dropout works by randomly setting the outgoing edges of hidden units.