Skin Cancer Detection and Consultation System

Overview

This repository contains the code for a comprehensive skin cancer detection and consultation system. The application leverages advanced NLP techniques and image analysis to provide an efficient and user-friendly solution for patients concerned about skin health. Key features include an NLP chatbot, image upload and analysis, and consultation booking for high-risk predictions.

Features

NLP Chatbot

• Engages in a conversation with the patient to understand their skin problems using advanced NLP techniques.
• Extracts relevant information from the patient's responses to assist in diagnosis.

Image Upload

• Allows patients to upload images of the affected skin area.
• Supports various image formats for user convenience.

Patient Metadata

• Patients can add metadata such as age, gender, and area of localization.
• Enhances prediction accuracy by incorporating patient-specific details.

Image Analysis

• Analyzes the uploaded image to predict the likelihood of skin cancer.
• Uses machine learning models trained on a diverse dataset of skin images.

Consultation Redirect

• Redirects patients with high-risk predictions to a kiosk page.
• Facilitates booking an appointment with available doctors for further consultation.

Setup & Installation

Prerequisites:

• Python 3.8+
• Django 3.x
• Pip

Installation Steps:

1. Clone the Repository ```sh git clone https://github.com/yourusername/skin-cancer-detection.git cd skin-cancer-detection ```
2. Create and Activate Virtual Environment ```sh python -m venv env env\Scripts\activate.bat # On Windows source env/bin/activate # On Unix or MacOS ```
3. Install Dependencies ```sh pip install -r requirements.txt ```
4. Apply Migrations ```sh python manage.py migrate ```
5. Run the Development Server ```sh python manage.py runserver ```

Usage

1. Run the server as described in the installation steps.
2. Access the application at `http://127.0.0.1:8000/\`.
3. Interact with the NLP chatbot to describe your skin condition.
4. Upload an image of the affected skin area.
5. Add metadata such as age, gender, and area of localization.
6. Review the analysis results to check the likelihood of skin cancer.
7. Book a consultation if the prediction indicates high risk.

Contributing

1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/YourFeature`).
3. Commit your changes (`git commit -am 'Add some feature'`).
4. Push to the branch (`git push origin feature/YourFeature`).
5. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any inquiries or issues, please contact [[email protected]].