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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.
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Full transcript