PredBrainTumor

A deep learning model for predicting brain tumor from MRI images using TensorFlow Convolutional Neural Network (CNN). Transfer learning is used to train the model. The model has four classes: meningioma, glioma, pituitary tumor, and no tumor with 98% prediction accuracy.

The model is deployed on here: https://btpred.soleyman.xyz

Built With

• TensorFlow - The machine learning framework used
• Keras - The high-level neural networks API used
• EfficientNet - For transfer learning
• Flask - The web framework used
• OpenCV - For image processing
• Matplotlib, Seaborn - For data visualization and analysis

Run Locally

• Clone the project git clone https://github.com/IMSoley/PredBrainTumor
• Install dependencies pip install -r requirements.txt
• Create upload dir mkdir static/uploads
• Run the app python app.py
• Visit http://127.0.0.1:5000

The model folder contains the trained model. Model testing data is in the testing folder. These data can be used to test the model locally or on the deployed website.

Description

The model is trained on the Brain Tumor Classification (MRI) dataset from Kaggle. The model is trained on 2,937 images and tested on 327 images. The model has 98% accuracy on the test set.

Sample images from the dataset

Model is defined as follows

    # EfficientNetB0 a convolutional neural network that is trained on more than a million images from the ImageNet database
    effnet = EfficientNetB0(weights='imagenet', # using the weights from the ImageNet database
                        include_top=False,
                        input_shape=(image_size, image_size, 3))
    # effnet output is the input to the model
    model = effnet.output
    model = tf.keras.layers.GlobalAveragePooling2D()(model)
    model = tf.keras.layers.Dropout(rate=0.5)(model)
    # this is the output layer with 4 classes
    model = tf.keras.layers.Dense(4, activation=tf.keras.activations.softmax)(model)
    model = tf.keras.models.Model(inputs=effnet.input, outputs=model)

Epoch vs Training and Validation Accuracy/Loss

Model classificaion report

    # calling the classification report function
    get_classification_report()

Confusion matrix

    # calling the confusion matrix function
    cm_analysis(y_test_new, prediction, "fig", classes, labels)

Note: The model is trained on Kaggle's GPU. It takes approximately 5 minutes to train the model for 12 epochs.

Deployment

Home page of the deployed model's app

Prediction for each class

glioma_tumor(0)	no_tumor(1)	meningioma_tumor(2)	pituitary_tumor(3)

License

MIT License