{"id":24819236,"url":"https://github.com/sidd-77/kidney-disease-classification","last_synced_at":"2026-04-11T02:55:07.121Z","repository":{"id":231332691,"uuid":"781474650","full_name":"Sidd-77/kidney-disease-classification","owner":"Sidd-77","description":"End-to-end deep learning project...","archived":false,"fork":false,"pushed_at":"2024-04-06T11:23:05.000Z","size":55081,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-25T20:41:34.355Z","etag":null,"topics":["docker","dvc","keras","mlflow","notebook","streamlit","tensforflow"],"latest_commit_sha":null,"homepage":"https://kidney-disease-classification-sidd.streamlit.app/","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/Sidd-77.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":"2024-04-03T13:02:40.000Z","updated_at":"2024-04-09T16:48:34.000Z","dependencies_parsed_at":"2025-03-25T20:51:17.724Z","dependency_job_id":null,"html_url":"https://github.com/Sidd-77/kidney-disease-classification","commit_stats":null,"previous_names":["sidd-77/kidney-disease-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Sidd-77/kidney-disease-classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sidd-77%2Fkidney-disease-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sidd-77%2Fkidney-disease-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sidd-77%2Fkidney-disease-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sidd-77%2Fkidney-disease-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Sidd-77","download_url":"https://codeload.github.com/Sidd-77/kidney-disease-classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sidd-77%2Fkidney-disease-classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":272922274,"owners_count":25015766,"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":"2025-08-30T02:00:09.474Z","response_time":77,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","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":["docker","dvc","keras","mlflow","notebook","streamlit","tensforflow"],"created_at":"2025-01-30T17:57:06.908Z","updated_at":"2026-04-11T02:55:02.103Z","avatar_url":"https://github.com/Sidd-77.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Kidney CT Scan Image Classification using VGG16\n\n  \n\nThis project is an end-to-end deep learning application that uses the VGG16 model for transfer learning to classify kidney CT scan images. The dataset used is sourced from Kaggle. The project also incorporates MLflow and DagsHub for experiment tracking, and DVC for data versioning and pipeline management.\n\n## Experiment-Tracking\n - Dagshub URL : https://dagshub.com/Sidd-77/kidney-disease-classification\n - Mlflow URL: https://dagshub.com/Sidd-77/kidney-disease-classification.mlflow\n  \n\n\n## Tech Stack\n\n  \n\n- **Deep Learning**: TensorFlow, Keras\n\n- **Transfer Learning**: VGG16 model\n\n- **Experiment Tracking**: MLflow, DagsHub\n\n- **Data Versioning**: DVC, GIT\n\n- **Containerization**: Docker\n\n- **Dataset**: [Kaggle CT Kidney Dataset](https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone/data)\n\n## Data Pipeline\n![Pipeline Structure](images/Pipeline.png)\n\n\n## Model Evalutaion\n![Model Evalutaion from mlflow](images/Results.png)\n\n## Setup and Installation \n\n  \n\n1. Clone the repository:\n\n```shell\n\ngit clone https://github.com/Sidd-77/kidney-disease-classification.git\n\n```\n\n  \n\n2. Navigate to the project directory:\n\n```shell\n\ncd kidney-disease-classification\n\n```\n\n  \n\n3. Install the required dependencies:\n\n```shell\n\npip install -r requirements.txt\n\n```\n\n  \n\n4. Run DVC pipeline:\n\n```shell\n\ndvc repro\n\n```\n\n  \n  \n\n## Running the Application\n\n  \n\n1. To train the model, run:\n\n```shell\n\npython main.py\n\n```\n\n  \n\n2. To start the Streamlit application, run:\n\n```shell\n\nstreamlit run application.py\n\n```\n\n  \n\n## Docker\n\n  \n\nA Dockerfile is provided if you wish to build a Docker image.\n\n  \n\n1. Build the Docker image:\n\n```shell\n\ndocker build -t \u003cimage-name\u003e  .\n\n```\n\n  \n\n2. Run the Docker container:\n\n```shell\n\ndocker run -p 8501:8501 \u003cimage-name\u003e\n\n```\n\n  \n\nThe application will be accessible at `http://localhost:8501`.\n\n  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsidd-77%2Fkidney-disease-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsidd-77%2Fkidney-disease-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsidd-77%2Fkidney-disease-classification/lists"}