Pneumonia Detection Web App
This web app allows users to upload chest X-ray images and determines the likelihood of pneumonia using a trained deep learning model.
Features
- Image upload (JPEG, PNG)
- Real-time pneumonia prediction from uploaded X-rays
- Probability estimates for both Normal and Pneumonia classes
- Displays the uploaded image with the predicted class
Technologies
- Python
- Flask
- TensorFlow/Keras
- HTML/CSS
- JavaScript (for basic image preview)
How to Run Locally
-
Clone the repository
git clone https://github.com/Tirth-70/Pneumonia-Detection.git
-
Install required packages:
cd Pneumonia-Detection pip install -r requirements.txt -
Run the application:
python app.py
-
Open your web browser and navigate to:
http://localhost:8080
Directory Structure
- static
style.css: Contains CSS styles for the web app.uploads: Stores uploaded images.
- templates
index.html: Contains the HTML structure and form.
app.py: Main Flask application file.requirements.txt: Lists required Python dependencies.generator.ipynb: (Presumably) Your training notebook, consider renaming ittrain.ipynbor similar if that's more accuratebest_model1.h5: (Presumably) Contains your best trained model.
Model Training
The generator.ipynb file (or a similarly named train.ipynb) presumably contains the code used to train the deep learning model. Details about the training dataset, preprocessing steps, and model architecture can be added to the README or included in a separate training_notes.txt file for more thorough documentation.
Contributing
[Feel free to suggest improvements or raise issues if you encounter any bugs.] (Replace with your own contribution guidelines, if desired).
Disclaimer
<<<<<<< HEAD This project is intended for educational and demonstration purposes. It is not a substitute for professional medical advice.
This project is intended for educational and demonstration purposes. It is not a substitute for professional medical advice.
dbba9d4 (Updated README)