LifeBeat - Lung Cancer Detection & Health Companion

Welcome to LifeBeat, an innovative healthcare application that helps users detect a specific type of lung cancer using chest X-rays. LifeBeat simplifies healthcare by integrating advanced cancer detection technology and providing users with tools to book appointments with doctors.

The app was developed by the team Error 404 during the HackStreet Hackathon, where we proudly secured the 2nd Runner-Up position. This README will guide you through the app’s features, current functionalities, and our roadmap for future enhancements.

Features

1. Lung Cancer Detection

• LifeBeat leverages a TensorFlow Lite (TFLite) model to analyze chest X-rays and detect a specific type of lung cancer.
• Users can upload chest X-rays for quick, reliable, and accurate diagnosis, aiding in early detection and timely medical intervention.

2. Book Appointments

• LifeBeat allows users to book appointments with specialists directly through the app.
• The easy-to-use appointment system simplifies the process of connecting with healthcare professionals.

Future Plans

1. Expanding Disease Detection

• Enhancements to our TFLite model will allow LifeBeat to detect additional types of diseases beyond lung cancer.
• Continuous improvement in detection accuracy and expanding the scope of healthcare services.

2. Chatbot Integration

• A smart chatbot will be integrated to suggest doctors based on users' symptoms.
• This will provide a personalized and guided healthcare experience.

3. Pharmacy Integration

• LifeBeat will include a pharmacy section, enabling users to order medicines directly through the app.
• This will create an all-in-one healthcare platform, eliminating the need for multiple apps.

Screenshots

How to Use

1. Install the App: [App Store / Google Play link]
2. Sign Up: Create an account and complete your profile.
3. Upload X-ray: Go to the detection section and upload your chest X-ray.
4. View Results: LifeBeat will analyze the X-ray and provide results.
5. Book an Appointment: Schedule an appointment with a doctor from within the app.
6. Stay Tuned for Updates: As we roll out more features, LifeBeat will evolve into a comprehensive healthcare solution.

Technologies Used

• Front-End: XML (for designing the user interface)
• Back-End: Kotlin (for backend logic and integration)
• Machine Learning: TensorFlow Lite (TFLite) for on-device X-ray analysis and cancer detection
• Database: Firebase Firestore for real-time data management
• APIs: Firebase Auth for user authentication, Google Cloud Healthcare API for secure data handling

Contribution

We welcome contributions! Feel free to submit a pull request or open an issue if you have suggestions for improvements or features.

Team Error 404

• App Dev : Vaibhav Sharma
• UI/UX :Harsh Kaushik
• ML Model : Samarpita Das
• Database Management : Ritika

This project was developed during the HackStreet Hackathon 2024, where we earned the 2nd Runner-Up position.

Thank you for your interest in LifeBeat! Stay connected for updates as we continue to enhance and expand the app's capabilities.