Rosella - PCOS Detection & Management App

A comprehensive mobile health application built with Flutter and Firebase that leverages Machine Learning and Deep Learning to predict and detect Polycystic Ovary Syndrome (PCOS) in women.

Demo

Rosella.Execution.Video.mp4

About The Project

Rosella is an intelligent healthcare application designed to assist in early detection and management of PCOS through dual prediction mechanisms. The app combines symptom-based ML prediction with ultrasound image analysis using deep learning, providing a comprehensive diagnostic support tool.

Features

• Dual PCOS Detection System
  • ML-based prediction using clinical parameters and symptoms
  • DL-based ultrasound image analysis for PCOS detection
• User Authentication - Secure login and signup using Firebase Authentication
• Real-time Database - User data management with Firebase Firestore
• Health Tracking - Monitor symptoms and health metrics over time
• User-Friendly Interface - Intuitive design for seamless user experience
• Personalized Results - Detailed prediction reports and recommendations

Built With

• Frontend: Flutter (Dart)
• Backend: Firebase
  • Firebase Authentication
  • Cloud Firestore
  • Firebase Storage
• Machine Learning:
  • ML Model - Symptom-based PCOS prediction
  • DL Model - Ultrasound image classification using CNN
• Platforms: Android & iOS

Prerequisites

Before running this project, ensure you have:

• Flutter SDK (>=3.0.0)
• Dart SDK (>=2.17.0)
• Firebase CLI
• Android Studio / VS Code
• Android SDK / Xcode (for iOS)

Getting Started

Installation

1. Clone the repository

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

2. Install dependencies

flutter pub get

3. Configure Firebase

• Create a new Firebase project at Firebase Console
• Add Android/iOS app to your Firebase project
• Download google-services.json (Android) and GoogleService-Info.plist (iOS)
• Place the files in their respective directories

4. Run the app

flutter run

ML/DL Models

Model 1: PCOS Prediction (ML)

• Type: Classification model using traditional ML algorithms
• Input: Clinical parameters (age, BMI, menstrual cycle irregularity, hormone levels, etc.)
• Output: PCOS probability score and risk assessment

Model 2: Ultrasound Image Analysis (DL)

• Type: Convolutional Neural Network (CNN)
• Input: Uterus/ovarian ultrasound images
• Output: PCOS presence detection with confidence score
• Accuracy: Optimized for medical image classification

Project Structure

lib/
├── models/          # ML/DL model integration
├── screens/         # App screens and UI
├── services/        # Firebase and API services
├── widgets/         # Reusable UI components
├── utils/           # Helper functions and constants
└── main.dart        # App entry point

Use Cases

• Early PCOS screening for at-risk women
• Supplementary diagnostic tool for healthcare providers
• Health monitoring and symptom tracking
• Educational resource about PCOS

Disclaimer

This application is designed as a supportive tool and should not replace professional medical diagnosis. Always consult with qualified healthcare professionals for accurate diagnosis and treatment of PCOS.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.