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Project overview
In this project, you’ll act as a Deep Learning Engineer for a company developing technologies to help hospitals diagnose patients. Using a dataset of chest X-rays from children, you’ll build and train convolutional neural network (CNN) models to accurately classify whether an X-ray shows signs of pneumonia.
This project allows you to apply your knowledge of CNNs and transfer learning to a real-world medical imaging challenge. You’ll gain hands-on experience in loading and preprocessing image data, building CNN architectures, and leveraging pre-trained models to boost performance. By iterating on your models and comparing different approaches, you’ll develop valuable skills in deep learning for computer vision.
**Objective:** Develop CNN models trained on X-ray images to accurately detect pneumonia, demonstrating the power of deep learning in assisting medical diagnosis.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Understanding the components and workings of CNNs for image classification
- Building and training basic CNNs using Keras for multi-class image classification
- Implementing early stopping, dropout, and batch normalization to reduce overfitting
- Applying transfer learning by fine-tuning pre-trained CNNs for new tasks
Projects steps
Step 1: Introduction
Step 2: Data Exploration
Step 3: First Model: Simple CNN I
Step 4: First Model: Simple CNN II
Step 5: Second Model: Transfer Learning
Step 6: Evaluating on the Test Set
Step 7: Next Steps
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