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.