This Repo contains the classification model built on MR images for the classification of brain tumor.

1. The data contains 4 classes:
   • No Tumor
   • Pituitary
   • Glioma
   • Meningioma
2. The datset can be found here.
3. The no_tumor class contains half the examples than the other 3 classes. To mitigate this problem, the data from Br35H is used to increase the examples in the no_tumor class and its size is made equal to the other 3 classes.

• The architecture used for the classification of is Pre-trained EfficientNet-B0.
• Optimizer: Adam
• Loss: Categorical CrossEntropy

The model is evaluated on 394 test images, unseen by the trained model before.

1. Version 1:
   • The training and validation accuracy came out to be ~95%.
   • The test accuracy proved to be ~71% yet.
   • The worst learned class being no_tumor, probably because of class imbalance in the training data where no_tumor is almost half the amount of other classes in the original data.
2. Version 2
   • Added gaussian Noise in training data.
   • The training and validation accuracy came out to be ~97%.
   • The test accuracy proved to be ~76%.