Detecting Brain Tumors with Deep Learning

Nov 23, 2024

Detecting Brain Tumors Using Deep Learning

Introduction

  • Presenter: Junaid from Edureka
  • Session Focus: Detecting brain tumors using deep learning.
  • Agenda:
    • Understanding deep learning
    • Processing images using deep learning
    • Building a deep learning model for brain tumor detection
    • Exploring pre-trained models and transfer learning

What is Deep Learning?

  • A subset of machine learning algorithms inspired by the human brain.
  • Effective with large datasets; performance improves with more data.
  • Difference from traditional machine learning:
    • Deep learning uses tensors (multi-dimensional arrays) instead of flattened arrays.
    • Better at capturing features within images compared to traditional algorithms.

Perceptron

  • Basic deep learning algorithm used for binary classification.
  • Inspired by the human neuron structure.
  • Uses multiple inputs in matrix form and calculates probabilities to determine output class.

Multi-Layer Perceptron

  • Composed of multiple perceptrons.
  • Better than single-layer perceptrons for capturing complex patterns and features in data.

Image Processing Using Deep Learning

  • Traditional methods (single-layer and multi-layer perceptrons) lead to overfitting and poor performance.
  • Convolutional Neural Networks (CNN) are introduced for better image processing.

CNN Structure

  • Convolutional Filter: Extracts features from images using patterns matching.
  • Pooling Layer: Reduces dimensionality; types include Max Pooling and Average Pooling.
  • Padding Layer: Maintains image dimensions to preserve features at the corners.
  • Flattening Operation: Converts 2D feature maps to a 1D array for dense layers.

Building a CNN for Brain Tumor Detection

  • Use MRI images to train the model.
  • Key steps:
    • Importing necessary libraries (NumPy, Matplotlib, Keras, etc.)
    • Data preprocessing, including loading and augmenting data.
    • Splitting data into training, validation, and test sets.
    • Building the CNN model with layers (convolutional, pooling, dense).
    • Compiling and training the model.

Model Evaluation

  • Accuracy achieved: ~83% initially.
  • Overfitting observed; need for better model optimization through methods like transfer learning.

Transfer Learning

  • Concept: Transfer knowledge from pre-trained models (e.g., MobileNet, ResNet) to improve accuracy.
  • Benefits:
    • Saves training time and computational resources.
    • Improved performance on specific tasks like brain tumor detection.
  • Implemented by using Keras' pre-trained models and adjusting the last layers for the specific task.

Conclusion

  • Final model accuracy improved to ~97% using transfer learning.
  • Successful predictions on new images demonstrated.
  • Encouragement to engage with the content through comments and questions.
  • Reminder to subscribe to Edureka channel for more learning content.