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https://github.com/marknature/computer-vision-project
This project focuses on developing a deep learning model for image classification to diagnose medical conditions using chest X-ray images. The goal is to classify images as either normal or pneumonia.
https://github.com/marknature/computer-vision-project
computer-vision cv jupyter-notebook opencv pandas python
Last synced: about 2 months ago
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This project focuses on developing a deep learning model for image classification to diagnose medical conditions using chest X-ray images. The goal is to classify images as either normal or pneumonia.
- Host: GitHub
- URL: https://github.com/marknature/computer-vision-project
- Owner: marknature
- Created: 2024-06-06T22:00:36.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-13T17:45:46.000Z (7 months ago)
- Last Synced: 2024-06-13T21:13:54.125Z (7 months ago)
- Topics: computer-vision, cv, jupyter-notebook, opencv, pandas, python
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
- Size: 86.4 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Computer-Vision-Project
## Artifictual Intelligence Intern - Medical Image Classification
### Project Description
This project focuses on developing a deep learning model for image classification to diagnose medical conditions using chest X-ray images. The goal is to classify images as either normal or pneumonia.
### Project Structure
- **data/**: Contains training, testing, and validation datasets.
- **models/**: Stores the trained model.
- **notebooks/**: Jupyter notebooks for data preprocessing and visualization.
- **results/**: Contains evaluation metrics.
- **src/**: Source code for data preprocessing, model training, and evaluation.
- **README.md**: Project description and setup instructions.
- **requirements.txt**: List of required dependencies.
### Project Directory Structure
```
medical_image_classification/
│
├── data/
│ ├── train/
│ ├── test/
│ └── val/
│
├── models/
│ └── model.h5
│
├── notebooks/
│ └── data_preprocessing.ipynb
│
├── results/
│ └── evaluation_metrics.txt
│
├── src/
│ ├── data_preprocessing.py
│ ├── model_training.py
│ ├── model_evaluation.py
│ └── utils.py
│
├── README.md
└── requirements.txt
```
### Setup Instructions
1. **Install Dependencies**:
```sh
pip install -r requirements.txt
```
2. **Download and Prepare Dataset**:
Download the Chest X-ray Images (Pneumonia) dataset from [Kaggle](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia) and place it in the `data/` directory.
3. **Preprocess Data**:
Run the Jupyter notebook `data_preprocessing.ipynb` to preprocess the images.
4. **Train the Model**:
```sh
python src/model_training.py
```
5. **Evaluate the Model**:
```sh
python src/model_evaluation.py
```
6. **View Results**:
Evaluation metrics will be saved in `results/evaluation_metrics.txt`.
```
### `requirements.txt`
```plaintext
tensorflow
numpy
matplotlib
pillow
streamlit
scikit-learn
```
#### Instructions to Run the System
1. **Install Dependencies**:
```sh
pip install -r requirements.txt
```
2. **Download and Prepare Dataset**:
Download the Chest X-ray Images (Pneumonia) dataset from [Kaggle](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia) and place it in the `data/` directory.
3. **Preprocess Data**:
Run the Jupyter notebook `data_preprocessing.ipynb` to preprocess the images.
4. **Train the Model**:
```sh
python src/model_training.py
```
5. **Evaluate the Model**:
```sh
python src/model_evaluation.py
```
6. **View Results**:
Evaluation metrics will be saved in `results/evaluation_metrics.txt`.