{"id":20119872,"url":"https://github.com/marknature/computer-vision-project","last_synced_at":"2026-04-12T09:03:03.865Z","repository":{"id":243138655,"uuid":"811580022","full_name":"marknature/Computer-Vision-Project","owner":"marknature","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.","archived":false,"fork":false,"pushed_at":"2024-06-13T17:45:46.000Z","size":90569,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-02T20:15:30.991Z","etag":null,"topics":["computer-vision","cv","jupyter-notebook","opencv","pandas","python"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/marknature.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-06T22:00:36.000Z","updated_at":"2024-06-13T17:45:49.000Z","dependencies_parsed_at":"2025-01-13T07:21:38.330Z","dependency_job_id":null,"html_url":"https://github.com/marknature/Computer-Vision-Project","commit_stats":null,"previous_names":["marknature/computer-vision-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/marknature/Computer-Vision-Project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marknature%2FComputer-Vision-Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marknature%2FComputer-Vision-Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marknature%2FComputer-Vision-Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marknature%2FComputer-Vision-Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/marknature","download_url":"https://codeload.github.com/marknature/Computer-Vision-Project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marknature%2FComputer-Vision-Project/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266786798,"owners_count":23983871,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-24T02:00:09.469Z","response_time":99,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","cv","jupyter-notebook","opencv","pandas","python"],"created_at":"2024-11-13T19:17:35.418Z","updated_at":"2026-04-12T09:03:03.823Z","avatar_url":"https://github.com/marknature.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Computer-Vision-Project\n## Artifictual Intelligence Intern - Medical Image Classification\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"interncareers_logo.jpeg\" alt=\"logo\"\u003e\n\u003c/p\u003e\n\n### Project Description\nThis 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.\n\n### Project Structure\n- **data/**: Contains training, testing, and validation datasets.\n- **models/**: Stores the trained model.\n- **notebooks/**: Jupyter notebooks for data preprocessing and visualization.\n- **results/**: Contains evaluation metrics.\n- **src/**: Source code for data preprocessing, model training, and evaluation.\n- **README.md**: Project description and setup instructions.\n- **requirements.txt**: List of required dependencies.\n\n### Project Directory Structure\n```\nmedical_image_classification/\n│\n├── data/\n│   ├── train/\n│   ├── test/\n│   └── val/\n│\n├── models/\n│   └── model.h5\n│\n├── notebooks/\n│   └── data_preprocessing.ipynb\n│\n├── results/\n│   └── evaluation_metrics.txt\n│\n├── src/\n│   ├── data_preprocessing.py\n│   ├── model_training.py\n│   ├── model_evaluation.py\n│   └── utils.py\n│\n├── README.md\n└── requirements.txt\n```\n\n### Setup Instructions\n1. **Install Dependencies**:\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n2. **Download and Prepare Dataset**:\n   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.\n\n3. **Preprocess Data**:\n   Run the Jupyter notebook `data_preprocessing.ipynb` to preprocess the images.\n\n4. **Train the Model**:\n   ```sh\n   python src/model_training.py\n   ```\n\n5. **Evaluate the Model**:\n   ```sh\n   python src/model_evaluation.py\n   ```\n\n6. **View Results**:\n   Evaluation metrics will be saved in `results/evaluation_metrics.txt`.\n```\n\n### `requirements.txt`\n```plaintext\ntensorflow\nnumpy\nmatplotlib\npillow\nstreamlit\nscikit-learn\n```\n\n#### Instructions to Run the System\n\n1. **Install Dependencies**:\n   ```sh\n   pip install -r requirements.txt\n   ```\n\n2. **Download and Prepare Dataset**:\n   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.\n\n3. **Preprocess Data**:\n   Run the Jupyter notebook `data_preprocessing.ipynb` to preprocess the images.\n\n4. **Train the Model**:\n   ```sh\n   python src/model_training.py\n   ```\n\n5. **Evaluate the Model**:\n   ```sh\n   python src/model_evaluation.py\n   ```\n\n6. **View Results**:\n   Evaluation metrics will be saved in `results/evaluation_metrics.txt`.\n   \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarknature%2Fcomputer-vision-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarknature%2Fcomputer-vision-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarknature%2Fcomputer-vision-project/lists"}