This project aims to develop a machine learning model to classify chest X-ray images as either pneumonic or normal.
Pneumonia is a lung infection that can be life-threatening if left untreated. Early and accurate diagnosis is crucial for effective treatment. This project leverages machine learning techniques to classify chest X-ray images as pneumonic or normal, assisting healthcare professionals in the diagnosis process.
To run this project, you'll need Python 3.x and the following libraries installed:
- TensorFlow
- Keras
- NumPy
- Matplotlib
- OpenCV
You can install the required libraries using pip:
- Clone the repository:
- Navigate to the project directory:
- Run the Jupyter Notebook:
- Open the
pneumonia_detection.ipynbnotebook and follow the instructions.
The project employs a Convolutional Neural Network (CNN) architecture for classifying chest X-ray images. The model is built and trained using TensorFlow and Keras libraries. Details about the model architecture, hyperparameters, and training process are provided in the Jupyter Notebook.
The dataset used in this project is taken from the study "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning" (https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5). The dataset consists of 5216 subsets of chest X-ray images, which can be found on Kaggle (https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia).
The dataset is preprocessed and split into training and validation sets before being fed into the machine learning model.
The trained model achieved an accuracy of at least 0.9 on the validation set. Upcoming are performance metrics, such as precision, recall, and F1-score, along with visualizations and examples of the model's predictions.
Contributions to this project are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository.
This project is licensed under the MIT License.