{"id":19599147,"url":"https://github.com/nishant2018/malaria-detection","last_synced_at":"2026-02-20T15:31:57.381Z","repository":{"id":233538149,"uuid":"787412672","full_name":"Nishant2018/Malaria-Detection","owner":"Nishant2018","description":"This is a web application built with Flask for detecting malaria in microscopic images of blood samples. It uses a deep learning model trained on TensorFlow/Keras to classify images as either infected (parasitized) or uninfected.","archived":false,"fork":false,"pushed_at":"2024-06-10T10:44:54.000Z","size":9302,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-19T11:53:45.086Z","etag":null,"topics":["cnn","cnn-classification","cnn-keras","deep-learning","image-classification","image-processing","supervised-learning"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/code/endofnight17j03/detecting-malaria-cnn","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/Nishant2018.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,"zenodo":null}},"created_at":"2024-04-16T13:27:18.000Z","updated_at":"2024-06-10T10:46:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"36d1fc76-edff-4c03-954c-6ee04b0b5712","html_url":"https://github.com/Nishant2018/Malaria-Detection","commit_stats":null,"previous_names":["nishant2018/malaria-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Nishant2018/Malaria-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nishant2018%2FMalaria-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nishant2018%2FMalaria-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nishant2018%2FMalaria-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nishant2018%2FMalaria-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Nishant2018","download_url":"https://codeload.github.com/Nishant2018/Malaria-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Nishant2018%2FMalaria-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29655304,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-20T09:27:29.698Z","status":"ssl_error","status_checked_at":"2026-02-20T09:26:12.373Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["cnn","cnn-classification","cnn-keras","deep-learning","image-classification","image-processing","supervised-learning"],"created_at":"2024-11-11T09:09:05.761Z","updated_at":"2026-02-20T15:31:57.364Z","avatar_url":"https://github.com/Nishant2018.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Malaria Detection\n\nThis is a web application built with Flask for detecting malaria in microscopic images of blood samples. It uses a deep learning model trained on TensorFlow/Keras to classify images as either infected (parasitized) or uninfected.\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Introduction\n\nMalaria is a life-threatening disease caused by parasites transmitted to humans through the bites of infected mosquitoes. Early detection and treatment are crucial for effective management and prevention of complications. This project aims to provide a tool for automated malaria detection using machine learning.\n\n## Features\n\n- Upload images of blood samples for malaria detection.\n- Obtain predictions on whether the sample is infected (parasitized) or uninfected.\n- Simple and intuitive web interface.\n- Backend powered by Flask, with a deep learning model trained on TensorFlow/Keras.\n\n## Installation\n\n1. Clone the repository:\n\n```bash\ngit clone https://github.com/yourusername/malaria-detection.git\ncd malaria-detection\n```\n\n2. Install the required dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n3. Run the Flask application:\n\n```bash\npython app.py\n```\n\n4. Open your web browser and navigate to `http://localhost:5000` to access the application.\n\n## Usage\n\n1. Upload an image of a blood sample by clicking the \"Choose Image\" button.\n2. Click the \"Predict\" button to obtain the malaria detection result.\n3. The prediction result will be displayed below the \"Predict\" button.\n\n## Contributing\n\nContributions are welcome! If you would like to contribute to this project, feel free to fork the repository and submit a pull request. Please ensure that any contributions adhere to the following guidelines:\n\n- Make sure your code follows PEP 8 guidelines.\n- Provide detailed descriptions of the changes made in your pull request.\n- Ensure that your changes do not break existing functionality.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishant2018%2Fmalaria-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnishant2018%2Fmalaria-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishant2018%2Fmalaria-detection/lists"}