Describe your project

Project Description

Our mobile application leverages advanced machine learning algorithms to detect potential signs of cancer from X-ray images. Designed with user-friendliness in mind, the app aims to facilitate early detection and provide comprehensive support for individuals concerned about their health.

1. scope of solution

• AI-Based X-ray Analysis: The core feature is the ability to upload X-ray images for AI-driven detection of abnormalities suggestive of cancer.
• Resource Hub: Users can access educational materials about cancer types, prevention, and treatment options.
• Notification Reminders: The app provides timely reminders for regular screenings and check-ups to encourage proactive health management.
• Consultation Access: Users can easily schedule consultations with healthcare professionals through the app, ensuring they receive personalized advice and follow-up care.

2. out of scope of solution

• Diagnosis or Treatment: While the app aids in early detection, it does not provide definitive diagnoses or medical treatment recommendations. Users are advised to consult healthcare professionals for any concerns.
• Other Medical Imaging: The current focus is solely on X-ray analysis; other imaging modalities (e.g., MRI, CT scans) are not included in this version.
• Comprehensive Medical Records: The app does not manage or store users' complete medical histories or offer integration with existing health records systems.

3. future opportunities for solution

• Expansion to Other Imaging Types: Future versions could incorporate analysis from additional imaging modalities such as CT scans and MRIs to broaden diagnostic capabilities.
• Integration with Wearable Devices: The app could evolve to include data from wearables, monitoring user health metrics in real-time and offering personalized health insights.
• Collaborative Research: Partnering with medical insti

Challenges we ran into

Project Challenges

During the development of our mobile application, we encountered several challenges that impacted our progress and required strategic solutions:

1. KTX Version Compatibility

• Issue: The Kotlin Extensions (KTX) version we initially integrated was not supported by our project setup.
• Solution: We had to identify and update to a compatible KTX version that aligned with our dependencies, ensuring smoother functionality and performance.

2. Target SDK Support

• Issue: The targeted SDK version posed compatibility issues with certain libraries and features we aimed to implement.
• Solution: We evaluated our dependencies and made necessary updates to align with the latest stable SDK version, while also ensuring backward compatibility for users on older Android versions.

3. Integration Challenges

• Issue: Integrating the machine learning model with the app's architecture proved more complex than anticipated, leading to delays.
• Solution: We refined our architecture and improved documentation, facilitating better integration of the ML model and enhancing overall performance.

4. Testing and Validation

• Issue: Ensuring accurate detection and minimizing false positives/negatives during testing was challenging.
• Solution: We implemented a rigorous testing protocol, utilizing a diverse dataset to train the model and conducting thorough validation with healthcare professionals.

5. User Experience Considerations

• Issue: Balancing complex functionalities with a user-friendly interface was a key challenge.
• Solution: We conducted user feedback sessions and usability testing to iteratively refine the app's design, ensuring that it remained intuitive while delivering robust features.

These challenges provided valuable lessons that strengthened our development process and ultimately enhanced the quality of our application.