Brain Tumor Detection

In this project I used a dataset of brain MRI images. This dataset contained two classes, one class belongs to images which are healthy brains. The other class belongs to images of brains with tumor.

I dowloaded the dataset from link bellow:
adress: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
The class of healthy brains contained 154 images.
The class of images with tumor contained 155 images.

I used a CNN neural network to make a binary classifier. At first, I trained the model for 10 epochs:

and I fined tuned it for 30 more epochs.

I reached the value accuracy=0.92

#Model with Augmented Images

I augmented images by:

• rotating
• width and height shifting
• horizontal fliping
• shearing
• zooming
  An example of this augmentation is placed bellow:

Then I repeated the model training:

Use a Pretrained Architecture VGG16

I used a pretrained model, VGG16, I freezed it and then I added one dense layer and a sigmoid classifier layer. I trined it for 100 epochs.

The best achieved accuracy for validation during training was 0.94