https://github.com/mathavansg/human-disease-prediction
https://github.com/mathavansg/human-disease-prediction
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/mathavansg/human-disease-prediction
- Owner: MathavanSG
- Created: 2023-10-23T18:34:37.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-02T16:51:34.000Z (almost 2 years ago)
- Last Synced: 2025-01-09T08:56:19.318Z (10 months ago)
- Language: Jupyter Notebook
- Size: 85.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Human Disease Prediction
GitHub license
Overview
Human Disease Prediction is a machine learning project that uses transfer learning with ResNet-50 and ImageNet weights to predict various skin diseases. The project provides an easy-to-use web interface for users to upload images of skin conditions and get predictions for the following diseases:
'BA- cellulitis': 0
'BA-impetigo': 1
'FU-athlete-foot': 2
'FU-nail-fungus': 3
'FU-ringworm': 4
'PA-cutaneous-larva-migrans': 5
'VI-chickenpox': 6
'VI-shingles': 7
The application is deployed locally using Streamlit.
Project Structure
The project's structure is organized as follows:
bash
Copy code
├── app.py # Streamlit application source code
├── Disease_prediction.h5 # Pretrained model for disease prediction
├── requirements.txt # Project dependencies
├── screenshots/ # Screenshots of the application
├── README.md # This README file
Setup and Installation
Clone this repository to your local machine:
cd human-disease-prediction
Install the required Python packages:
pip install -r requirements.txt
Usage
Start the Streamlit application:
streamlit run app.py
Access the application by opening the provided URL in your web browser.
Upload an image of the skin condition you want to predict.
Click the "Predict" button to get the disease prediction.
Acknowledgments
ResNet-50 with ImageNet weights
Streamlit for the web interface# Human-Disease-Prediction