This repository contains the implementation of a Convolutional Neural Network (CNN) model to detect COVID-19 using chest X-ray images. The project demonstrates how deep learning techniques can assist in identifying COVID-19 cases with high accuracy.
This project was a group assignment for our Machine Learning subject. It highlights the application of CNNs in medical image analysis and aims to provide a foundation for further research and improvements in AI-based diagnostic tools.
- Trisha Sharma (229309215)
- Anahita Bhandari (229309186)
The model was trained and tested on a dataset containing chest X-ray images categorized into COVID-19 positive, normal, and pneumonia cases. The dataset was preprocessed to ensure compatibility with the CNN model.
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Data Preprocessing:
- Resized images for uniformity.
- Normalized pixel values for faster convergence.
- Split the data into training, validation, and test sets.
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Model Architecture:
- Built a CNN with multiple convolutional and pooling layers.
- Used ReLU activation and dropout for better generalization.
- Added fully connected layers to classify images.
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Training:
- Optimized the model using the Adam optimizer.
- Categorical cross-entropy as the loss function.
- Evaluated model performance using accuracy and loss metrics.
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Testing:
- Assessed the model on unseen test data.
- Generated a confusion matrix to analyze classification results.
- Achieved a high level of accuracy in classifying COVID-19 cases.
- The model demonstrated the potential of CNNs in medical diagnostics.
- Clone the repository:
git clone https://github.com/<your-username>/covid19-detection-cnn.git
- Install Requirements: pip install -r requirements.txt
- Atlast train and test the model
- Python
- TensorFlow/Keras
- OpenCV
- *NumPy, *pandas
- Matplotlib for visualization
This project was submitted as part of the coursework for our Machine Learning subject.
We extend our gratitude to our faculty and peers for their guidance and support.