BRAIN TUMOUR DETECTION

BUSINESS CASE: BASED ON BRAIN MRI IMAGES DATASET WE NEED PREDICT BRAIN TUMOUR

TASK: BINARY CLASSIFICATION

DATA SUMMARY

Total 274 Jpg Images are present In two classes

No ----> 119
Yes ----> 155

DEVICE THE PROJECT INTO MULTIPLE STEPS

Prepare training, validation and testing set
Get all classes labels
Visualise training images
Used CNN & VGG19 model
Model Compilation
Model Training
Model Evaluation
Model Saving
Prediction on test data

Architecture of prepare CNN model

Compile & Train Model

Use binary crossentropy loss with adam optimizer and metrics is accuracy
Balance the class weights
Train model of 80 epoch and use model checkpoint to save the best model
Plotting training, validation loss as well as accuracy

Achieved 88.24% testing accuracy

Used VGG19 transfer learning technique to get better accuracy

Import vgg19 library and set input image size & used imagnet dataset weight as well as not include fully connected layer at top
Freeze the existing weights
Add more layers with sigmoid activation function

Architecture of VGG19 model

Compile & Train model

Use binary crossentropy loss with adam optimizer and metrics is accuracy
Train model of 60 epoch and set the class weight
Plotting training, validation loss as well as accuracy

Save model

Achieved 92.16% testing accuracy