Project Info

VeriMed

Inspiration

The inspiration for VeriMed came from witnessing people selling and buying fake medications in Africa due to lack of resources and regulations. Abdoul has seen expired medicines being sold in villages, and a shocking statistic stands out: over 250,000 people die annually from counterfeit drugs, with up to 70% of medications being fake in some developing countries. As a team passionate about using technology to solve real-world problems, we were deeply moved by the thought that people are literally dying from fake medicines while we have the technology to prevent it. The idea of using smartphone cameras and AI to instantly detect counterfeit medicines felt like a perfect solution accessible, immediate, and life-saving.

What it does

Instant Detection: Point your phone at any medicine and get real-time authenticity verification AI Analysis: Uses multiple AI models to analyze packaging, pill appearance, and batch codes Offline Functionality: Works anywhere without internet connectivity Solves Critical Problem: 70% of medications are fake in some developing countries Global Accessibility: Works on any smartphone worldwide Life-Saving Potential: Prevents consumption of dangerous counterfeit medicines

How we built it

Our team approached this as a systematic, phase-based development process: Phase 1: Mobile Foundation Set up React Native/Expo project with TypeScript Implemented camera integration for medicine scanning Built user authentication and role-based access Created professional UI/UX with smooth navigation Phase 2: Data Collection & ML Setup Developed synthetic data generation system Built real-world data collection pipeline Implemented image preprocessing and augmentation Created quality assessment and metadata tracking Phase 3: ML Integration & Testing Designed multi-model CNN architecture (packaging, pill, batch code, fusion) Implemented real-time inference service with on-device processing Built comprehensive training pipeline with progress tracking Created model testing suite with performance validation Developed professional ML dashboard for training and monitoring

Challenges we ran into

Technical Challenges Dependency Hell: React Native/Expo projects have complex dependency requirements. We spent significant time resolving peer dependency conflicts, especially with TensorFlow.js and camera libraries. Mobile ML Performance: Getting ML models to run efficiently on mobile devices was challenging.

What we learned

This project was an incredible learning journey across multiple domains: Technical Skills React Native & Expo: Built a complete mobile application Machine Learning: Implemented real-time ML inference using TensorFlow.js and TensorFlow Lite Computer Vision: Developed custom CNN architectures for packaging, pill, and batch code analysis TypeScript: Achieved 97.3% type coverage for robust, maintainable code Firebase Integration: Set up authentication, database, and storage services ML & AI Concepts Multi-model Architecture: Designed separate CNNs for different analysis types Fusion Networks: Combined multiple model outputs for final decisions User Experience: Created intuitive interfaces for complex ML operations ##

Analysis

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Technology

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