MediInsight - A One Stop Medical Disease Diagnosis Powered by Machine Learning and Deep Learning

Overview

MediInsight is an innovative project that leverages the power of machine learning (ML) and deep learning (DL) to provide a comprehensive disease diagnosis solution. By utilizing various ML and DL models, MediInsight aims to accurately predict and diagnose a range of diseases based on user-provided data and images.

Live Application

Features

• Multiple Disease Predictions: Supports predictions for diabetes, breast cancer, heart disease, kidney disease, liver disease, malaria, and pneumonia.
• User-Friendly Interface: Easy-to-use web interface for uploading data and images.
• Automated Analysis: Processes and analyzes user inputs to provide quick and accurate disease predictions.
• Integration with ML/DL Models: Seamlessly integrates multiple machine learning and deep learning models for various diseases.

Demo

Check out the live demo: MediInsight

Installation

To run MediInsight locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/MediInsight.git
   cd MediInsight

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   pip install -r requirements.txt

3. Download the necessary models and place them in the models directory.

4. Run the application:

   python app.py

   The application will be accessible at http://127.0.0.1:5000/.

Usage

1. Home Page: Navigate to the home page to select the type of disease you want to diagnose.
2. Upload Data: Depending on the disease, upload the necessary data or image.
3. Get Prediction: Click on the predict button to get the diagnosis result.

Supported Diseases

1. Diabetes

• Requires 8 input values including BMI, Insulin, and Glucose levels.
• Uses a Random Forest model.

2. Breast Cancer

• Requires 22 input values.
• Uses a pre-trained ML model.

3. Heart Disease

• Requires 13 input values.
• Uses a Random Forest model.

4. Kidney Disease

• Requires 24 input values.
• Uses a pre-trained ML model.

5. Liver Disease

• Requires 10 input values.
• Uses a pre-trained ML model.

6. Malaria

• Requires an image upload.
• Uses a Convolutional Neural Network (CNN) model.

7. Pneumonia

• Requires an image upload.
• Uses a Convolutional Neural Network (CNN) model.

Code Highlights

Model Prediction Example

def predict(values, dic):
    # Example for diabetes prediction
    if len(values) == 8:
        model = pickle.load(open('models/diabetes_rf_model.pkl', 'rb'))
        values = np.asarray(values)
        return model.predict(values.reshape(1, -1))[0]
    # Add similar logic for other diseases...

Handling Image Uploads

@app.route("/malariapredict", methods=['POST', 'GET'])
def malariapredictPage():
    if request.method == 'POST':
        try:
            img_file = request.files['image']
            filename = secure_filename(img_file.filename)
            img_path = os.path.join('uploads', filename)
            img_file.save(img_path)
            
            img = tf.keras.utils.load_img(img_path, target_size=(150, 150))
            img = tf.keras.utils.img_to_array(img)
            img = np.expand_dims(img, axis=0)
            
            model = load_model("models/malaria_detect.h5")
            pred = np.argmax(model.predict(img))
            
            os.remove(img_path)  # Clean up by deleting the uploaded image
            
            return render_template('malaria_predict.html', pred=pred)
        except Exception as e:
            message = f"Error: {e}"
            return render_template('malaria.html', message=message)
    return render_template('malaria.html')

Contributions

We welcome contributions to enhance MediInsight. Here's how you can contribute:

1. Fork the repository on GitHub.
2. Clone your forked repository to your local machine:

   git clone https://github.com/yourusername/MediInsight.git
   cd MediInsight

3. Create a new branch for your feature or bugfix:

   git checkout -b feature-branch

4. Make your changes and commit them:

   git commit -m "Description of your changes"

5. Push your changes to your fork:

   git push origin feature-branch

6. Open a Pull Request on the main repository.

For any issues or bug reports, please open an issue on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Sample images of the web application

Home Page

Diabetes Predictor

Breast Cancer Predictor

Malaria Predictor

Negative Result Page

Positive Result Page