This project is a web application that uses a machine learning model to diagnose skin diseases based on uploaded images. Users can upload images of their skin lesions and receive a prediction of the possible condition.
- Upload images of skin lesions.
- Diagnose the condition using a trained machine learning model.
- Display the predicted diagnosis and detailed information (if available).
- Allow users to report inaccurate results.
- Backend: Python, Flask
- Machine Learning: TensorFlow, Keras
- Frontend: HTML, CSS, JavaScript
- Other: None
skin-notebook/
│
├── data3/
│ ├── test/
│ └── train/
│
├── static/
│ ├── script.js
│ └── style.css
│
├── templates/
│ └── index.html
│
├── requirements.txt
├── app.py
├── disease_details.json
├── predict-colab.ipynb
├── predict.ipynb
├── skin_disease_cnn_model_newv2.keras
└── README.md
- Clone the repository:
git clone https://github.com/NienCongChua/skin-notebook.gitor click download to faster. - Download the directory data from the link:
data3oralternative data3to train the model. (This HAM10000 dataset is preprocessed by LewPie, you can check hisrepository) - Create a virtual environment:
python3.10 -m venv env - Activate the virtual environment:
source env/bin/activate - Install the required libraries:
pip install -r requirements.txt - Run the application:
python app.py
- Open a web browser and go to
http://127.0.0.1:5000/. - Choose an image of the skin lesion you want to diagnose.
- Click the "Upload and Predict" button.
- View the predicted diagnosis displayed below.
- If you have any questions, please click "Report".
- The accuracy of the model depends on the quality and quantity of the training data.
- This application should not be used for self-diagnosis or treatment.
- Always consult with a healthcare professional for accurate diagnosis and treatment.
- Richard Jacob - https://github.com/NienCongChua/
- Pham Thanh Dat - https://github.com/LewPie/
- If you have any problem with model
skin_disease_cnn_model_newv2.keras, you can download it frommy file