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