{"id":18075792,"url":"https://github.com/darshak-cloud/skin-disease-detection-using-machine-learning-with-ensemble-methods","last_synced_at":"2025-08-19T16:34:42.623Z","repository":{"id":257884656,"uuid":"873057847","full_name":"darshak-cloud/Skin-disease-detection-using-Machine-Learning-with-ensemble-methods","owner":"darshak-cloud","description":"This project detects nine skin diseases using machine learning and ensemble methods. It features a Flask web app for real-time image-based diagnosis via uploads or webcam.","archived":false,"fork":false,"pushed_at":"2024-10-15T17:06:00.000Z","size":21834,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-12-19T02:24:05.037Z","etag":null,"topics":["flask","html-css-javascript","matplotlib","python","seaborn","skimage","skin","skin-detection","skin-disease-detection","skins","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"https://github.com/darshak-cloud/Skin-disease-detection-using-ML-with-ensemble-methods/tree/main","language":"Python","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/darshak-cloud.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":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-15T14:28:10.000Z","updated_at":"2024-10-25T07:21:42.000Z","dependencies_parsed_at":"2024-10-16T22:23:18.402Z","dependency_job_id":null,"html_url":"https://github.com/darshak-cloud/Skin-disease-detection-using-Machine-Learning-with-ensemble-methods","commit_stats":null,"previous_names":["darshak-cloud/skin-disease-detection-using-ml-with-ensemble-methods"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/darshak-cloud%2FSkin-disease-detection-using-Machine-Learning-with-ensemble-methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/darshak-cloud%2FSkin-disease-detection-using-Machine-Learning-with-ensemble-methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/darshak-cloud%2FSkin-disease-detection-using-Machine-Learning-with-ensemble-methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/darshak-cloud%2FSkin-disease-detection-using-Machine-Learning-with-ensemble-methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/darshak-cloud","download_url":"https://codeload.github.com/darshak-cloud/Skin-disease-detection-using-Machine-Learning-with-ensemble-methods/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230365091,"owners_count":18214895,"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","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":["flask","html-css-javascript","matplotlib","python","seaborn","skimage","skin","skin-detection","skin-disease-detection","skins","sklearn","tensorflow"],"created_at":"2024-10-31T11:07:25.446Z","updated_at":"2024-12-19T02:24:18.918Z","avatar_url":"https://github.com/darshak-cloud.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Skin Disease Detection Using Machine Learning and Ensemble Methods\nThis project implements a machine learning system for detecting and classifying nine different skin diseases from images using ensemble methods to enhance accuracy.\n\n\nOverview\nThe goal of this project is to build a model that can accurately classify nine types of skin diseases from images, leveraging ensemble learning techniques. The system extracts features from images using Histogram of Oriented Gradients (HOG) and trains multiple models combined into an ensemble to improve classification performance. The final model is deployed in a Flask web application, allowing users to either upload an image or capture one through a webcam for real-time prediction.\n\n\nDataset\nThe dataset used for training and validation is publicly available on Kaggle. It contains images for nine different skin diseases, each with 90-100 images.\n•\tDataset: https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-classification-image-dataset/data\nSkin Diseases in the Dataset:\n1.\tActinic Keratosis\n2.\tAtopic Dermatitis\n3.\tBenign Keratosis\n4.\tDermatofibroma\n5.\tMelanocytic Nevus\n6.\tMelanoma\n7.\tSquamous Cell Carcinoma\n8.\tTinea Ringworm Candidiasis\n9.\tVascular Lesion\n\n\nKey Features\n•\tData Preparation: Preprocessing images for model training.\n•\tFeature Extraction: Using HOG to extract important features from images.\n•\tEnsemble Learning: Combining multiple models to improve classification accuracy.\n•\tFlask Deployment: Web application that allows image upload or live webcam input for skin disease detection.\n\n\nModel Training\nThe ensemble learning approach includes training various classifiers to achieve an optimal accuracy of 99-100%.\n\nWeb Application\nThe web application is built using Flask and allows users to:\n•\tUpload an image of skin for disease prediction.\n•\tUse a webcam to capture live images for real-time detection.\n\n\nInstallation\nTo run this project locally:\n1.\tClone this repository.\n2.\tInstall the required dependencies from requirements.txt: pip install -r requirements.txt\n3.\tRun the Flask application: python app.py\n\nFile Structure (If the file structure is not clearly visible or not in proper order use RAW to see it or you can see it with my repository which I already uploaded at starting folder name readmi)\n│folder name\n├── app/\n│   └── app.py               # Flask web application\n├── templates/\n│   ├── index.html           # Upload page\n│   └── result.html          # Results page\n├── static/\n│   ├── style.css            # CSS styling\n│   └── webcam.js            # Webcam functionality\n├── models/\n│   ├── # Trained ensemble model\n├── scripts/\n│   ├── data_preparation.py  # Data preparation script\n│   ├── feature_extraction.py# HOG feature extraction\n│   ├── model_training.py    # Model training script\n│   └── model_evaluation.py  # Model evaluatio[Skin Disease Detection Using Machine Learning and Ensemble Methods.docx](https://github.com/user-attachments/files/17381767/Skin.Disease.Detection.Using.Machine.Learning.and.Ensemble.Methods.docx)\nn script\n└── uploads/                 # Folder for storing uploaded images\n\n\nUsage\n•\tTo run this project from script folder First you have to set up my environment variable if you are using VScode. If not there is no need.\n•\tFor setting up myenv follow step: (make sure that you first delete myenv file that is already exist in the repository after copying or downloading)\n1.\tOpen Terminal in VS Code: Use the integrated terminal in VS Code.\n2.\tCreate a Virtual Environment: Run python -m venv myenv (replace myenv with your desired environment name).\n3.\tActivate the Virtual Environment:\nWindows: .\\myenv\\Scripts\\activate\nmacOS/Linux: source myenv/bin/activate\n•\tIt looks like: (myenv) PS C:\\Users\\[your path]\\GitHub\\Skin-disease-detection-using-ML-with-ensemble-methods\u003e\n•\tNow after training you just need to run app.py file.\n•\tNavigate to localhost:5000 in your web browser after running the Flask app.\n•\tUpload an image or use the webcam to get a skin disease prediction.\n\n\nLicense\nThis project is not open source. All rights reserved. Please do not distribute or use this project without permission.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdarshak-cloud%2Fskin-disease-detection-using-machine-learning-with-ensemble-methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdarshak-cloud%2Fskin-disease-detection-using-machine-learning-with-ensemble-methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdarshak-cloud%2Fskin-disease-detection-using-machine-learning-with-ensemble-methods/lists"}