What it does
Our app analyzes retinal images using AI to identify early signs of cancer. It provides instant feedback and directs users to consult specialists if needed, making early detection easier than ever.
How we built it
We used a combination of machine learning models trained on retinal image datasets, along with a mobile-friendly interface for quick image uploads. The AI runs in real-time to provide results in seconds.
Challenges we ran into
Acquiring and preprocessing high-quality retinal image data
Training the AI model to minimize false positives and negatives
Optimizing the app to work smoothly on low-end devices
Accomplishments that we're proud of
Developed an AI model capable of detecting retinal anomalies with high accuracy
Built a user-friendly app interface suitable for all age groups
Achieved real-time image processing without heavy device requirements
What we learned
The importance of clean, labeled datasets for AI accuracy
How to integrate AI models into mobile applications efficiently
Balancing performance and usability in a health-critical app
What's next for Cancer Detection App
Expand the dataset to improve detection across different demographics
Add features like risk tracking, history, and specialist appointment integration
Explore extending the technology to detect other eye diseases early
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