Pneumonia Prediction Using Hybrid Neural Networks is a deep learning project aimed at improving the accuracy of pneumonia diagnosis from chest X-ray images. This project integrates three advanced convolutional neural network (CNN) architectures—DenseNet201, InceptionResNetV2, and ResNet50—into a hybrid model to leverage their strengths for superior performance in detecting pneumonia.
- Hybrid Model: Combines DenseNet201, InceptionResNetV2, and ResNet50 to enhance diagnostic accuracy.
- High Accuracy: Achieves a training accuracy of 99.99% and a validation accuracy of 95.67%.
- Advanced Techniques: Utilizes feature concatenation, learning rate scheduling, and data augmentation for improved model performance.
| Model Name | Precision | Recall | Accuracy | F1 Score |
|---|---|---|---|---|
| DenseNet201 | 0.52 | 0.56 | 83.81 | 0.55 |
| InceptionResNetV2 | 0.53 | 0.58 | 80.61 | 0.56 |
| ResNet50 | 0.765 | 0.625 | 62.50 | 0.69 |
| Hybrid(Stacked) | 0.845 | 0.750 | 95.67 | 0.792 |
To set up the project environment, follow these steps:
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Clone the Repository
git clone https://github.com/yourusername/pneumonia-prediction.git cd pneumonia-prediction -
Create a Virtual Environment
python -m venv venv
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Activate the Virtual Environment On windows :
venv\Scripts\activate
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Install Dependencies
pip install -r requirements.txt
For more details on the project and related research, please visit this IEEE link.