Brain Tumor Classification

Classifying Brain Tumor using Advanced Machine Learning

Brain Tumor Classification

Classify tumor types using AI models

Project Abstract

Understanding our approach to brain tumor classification

Brain Tumor Classification using Deep Learning (CNN)

Brain tumors are among the most serious medical conditions that require early and accurate diagnosis for effective treatment. This project presents an advanced deep learning approach for automated brain tumor detection using MRI scan analysis.

Our system employs a Convolutional Neural Network (CNN) to perform binary classification, determining whether a brain tumor is present or absent in MRI scans. The model was trained on a comprehensive dataset of labeled MRI images with "Yes" and "No" classifications for tumor presence.

The implementation achieved an impressive 97.03% training accuracy and 92.16% validation accuracy, demonstrating the effectiveness of our CNN architecture in medical image analysis. The model processes 100x100 pixel MRI images and provides confident predictions with probability scores.

Tumor Categories

Our AI system can detect the presence of brain tumors in MRI scans

No Tumor
Normal
No Tumor

Normal brain tissue without any tumor presence. Healthy brain scans showing no abnormal growths or masses.

Tumor Present
Tumor Detected
Tumor Present

Brain scan showing presence of tumor tissue. Requires immediate medical attention and further analysis.

Interactive Prediction

Upload an MRI scan to classify brain tumor type

Upload MRI Image
Classification Results

Upload an image to see classification results

Our CNN Model Statistics

Performance metrics and training results

97.03%

Final Accuracy

Training Accuracy

92.16%

Validation Accuracy

Cross-validation Score

0.0855

Final Loss

Training Loss

0.3086

Validation Loss

Validation Loss

Training Progress

Data Preprocessing & Model Building

Our approach to building an effective brain tumor detection system

Dataset Collection
Step 1

Collected MRI brain scan images with binary labels (Yes/No for tumor presence)

  • Total Images: 200+ MRI scans
  • Tumor Present: 100 images
  • No Tumor: 100 images
  • Image Format: JPEG, PNG
  • Resolution: 100 x 100 pixels
Data Preprocessing
Step 2

Applied various preprocessing techniques to enhance image quality and normalize data

  • Image Resizing: 100 x 100 pixels
  • Normalization: Pixel values [0,1]
  • Data Augmentation: Rotation, Flip, Zoom
  • Train/Validation Split: 80/20
  • Batch Size: 32
Model Architecture
Step 3

Built a Sequential CNN model optimized for binary classification

  • Model Type: Sequential CNN
  • Total Parameters: 4,353,346
  • Model Size: 16.61 MB
  • Activation: ReLU, Softmax
  • Optimizer: Adam
Training Results
Step 4

Achieved excellent performance with high accuracy and low loss

  • Final Accuracy: 97.03%
  • Validation Accuracy: 92.16%
  • Final Loss: 0.0855
  • Validation Loss: 0.3086
  • Training Epochs: 10
Training Progress
Training Progress Logs

Training logs showing progressive improvement in accuracy and reduction in loss over 10 epochs

Model Architecture Summary
Model Architecture Details

Convolutional Layers

  • • Conv2D: 32 filters, 3x3 kernel
  • • MaxPooling2D: 2x2 pool size
  • • Conv2D: 64 filters, 3x3 kernel
  • • MaxPooling2D: 2x2 pool size

Dense Layers

  • • Flatten Layer
  • • Dense: 128 neurons, ReLU
  • • Dropout: 0.5 rate
  • • Dense: 2 neurons, Sigmoid

Model Summary

  • • Total Parameters: 4,353,346
  • • Trainable Parameters: 4,353,346
  • • Model Size: 16.61 MB
  • • Input Shape: (224, 224, 3)