🧠 NeuroVision: Brain Tumor MRI Classification with Deep Learning

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📌 Project Overview

This project focuses on automated brain tumor detection using MRI images.
We apply transfer learning with VGG16, a convolutional neural network (CNN) pre-trained on the ImageNet dataset, and fine-tune it to classify brain MRIs into:

• Meningioma
• Glioma
• Pituitary Tumor
• No Tumor

The trained model is deployed via a Streamlit app, where users can upload MRI images and get predictions with confidence scores.

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📂 Project Structure

brain-tumor-mri/
│
├── brain_tumour_detection.ipynb   # Training & evaluation (KaggleHub dataset)
├── streamlit_app.py                       # Deployment UI with Streamlit
├── requirements.txt                       # Dependencies
└── README.md                              # Documentation

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📊 Dataset

Dataset is fetched from KaggleHub:

import kagglehub
path = kagglehub.dataset_download("masoudnickparvar/brain-tumor-mri-dataset")

Structure:

Training/
   ├── glioma/
   ├── meningioma/
   ├── pituitary/
   └── notumor/
Testing/
   ├── glioma/
   ├── meningioma/
   ├── pituitary/
   └── notumor/

• Training samples: ~2870
• Testing samples: ~394
• Each subdirectory corresponds to a tumor class.

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🏗️ Model Architecture (Transfer Learning with VGG16)

We use VGG16, a pre-trained CNN on ImageNet, as a feature extractor and fine-tune its top layers.

🔹 Step 1: Base Model

from tensorflow.keras.applications import VGG16

base_model = VGG16(
    input_shape=(128,128,3),
    include_top=False,
    weights='imagenet'
)

• Input size: 128 × 128 × 3 (resized MRI images).
• include_top=False: removes VGG16’s fully connected (FC) head.
• weights="imagenet": initializes weights from ImageNet (~1.4M images, 1000 classes).

Mathematically, each convolutional layer applies:

where:

• W^{(k)}: convolutional kernel for feature map k
• x: input patch
• σ: activation (ReLU in VGG16)

🔹 Step 2: Freezing and Fine-Tuning

for layer in base_model.layers:
    layer.trainable = False

# Unfreeze top 3 layers
base_model.layers[-2].trainable = True
base_model.layers[-3].trainable = True
base_model.layers[-4].trainable = True

• Lower layers retain general features (edges, textures).
• Top 3 layers fine-tuned to capture domain-specific features of MRI tumors.

🔹 Step 3: Custom Classification Head

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dropout, Dense

model = Sequential([
    base_model,
    Flatten(),
    Dropout(0.3),
    Dense(128, activation='relu'),
    Dropout(0.2),
    Dense(len(unique_labels), activation='softmax')
])

• Flatten: Reshapes VGG16 feature maps (4 × 4 × 512) → (8192,).
• Dense(128, relu): Fully connected layer learns non-linear combinations of features.

• Dropout(0.3, 0.2): Randomly zeroes units during training, reducing overfitting.
• Output Layer: Softmax classifier for 4 classes:

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📒 Notebook (brain_tumour_detection_vs_code.ipynb)

Sections:

1. Dataset loading with KaggleHub
2. EDA: Class distributions, sample MRI visualization
3. Preprocessing: Resize, normalize, split
4. Model: VGG16 base + custom classifier
5. Training: Adam optimizer, categorical crossentropy loss

6. Evaluation: Accuracy, confusion matrix, classification report

7. Saving model in .h5 format

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🧮 Training Statistics

• Optimizer: Adam (β₁=0.9, β₂=0.999)
• Learning Rate: 1e-4
• Batch Size: 32
• Loss: Categorical Crossentropy
• Regularization: Dropout (0.3 & 0.2)

Performance:

• Training Accuracy: ~95%
• Test Accuracy: ~92%
• Balanced F1 scores across all classes

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🎨 Streamlit App (streamlit_app.py)

Interactive deployment UI:

• Upload MRI image (png/jpg/jpeg)
• Preprocessing: resize 128 × 128, normalize [0,1]
• Prediction from VGG16-based model
• Displays:
  • Label
  • Confidence
  • Uploaded image
  • Raw probabilities

Run:

streamlit run streamlit_app.py

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⚙️ Installation (VS Code)

# 1. Create environment
python -m venv .venv
.venv\Scripts\activate   # Windows
source .venv/bin/activate  # Mac/Linux

# 2. Install deps
pip install -r requirements.txt

# 3. Run app
streamlit run streamlit_app.py

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🚀 Deployment

• Local: http://localhost:8501
• LAN: http://:8501
• Global: deploy on Streamlit Cloud
• 

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🙌 Acknowledgements

• Dataset: Brain Tumor MRI Dataset
• Pre-trained Model: MRI Brain Tumor Model
• Base Architecture: VGG16

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