case-study
Dermaself Flutter Skin Analysis App
Flutter mobile app case study for a guided cosmetic skin-analysis flow with Firebase-backed account, intake, photo capture, and results screens.
Overview
Dermaself is a mobile CV case study organized as a native Flutter experience for Android and iOS, with a guided path from account setup through onboarding, questionnaire intake, selfie/device photo capture, analysis results, and home navigation. The engineering signal is the mobile architecture, Firebase integration, offline model runtime, ROI gating, tile-bounded wrinkle and fine-line processing, pore detection, and promotion decisions for a camera-heavy cosmetic analysis workflow.
What It Covers
- Structures the app into clean feature modules for auth, onboarding, questionnaire, photo capture, device capture, analysis, and home
- Uses Firebase services for account state, database records, image storage, analytics, messaging, and serverless extension points
- Builds a guided capture-to-results UX for camera-heavy cosmetic analysis without presenting the portfolio entry as a medical diagnostic claim
- Includes offline model runtime, ROI gating, and tile-bounded wrinkle/fine-line processing in the mobile delivery path
- Keeps debug-only segmentation notes separate from launch claims when a model should not be promoted
Stack And Topics
- Flutter
- Dart
- Firebase
- Riverpod
- GoRouter
- ONNX
- TFLite
- Mobile CV
- iOS
- Android
Public Signals
- Native targets: 2 Android and iOS app structure
- Feature modules: 7 auth, onboarding, questionnaire, photo capture, device capture, analysis, home
- Debug Dice ceiling: 0.975 mean 12-image overfit diagnostic, not holdout validation, 2026-05-12
- Promotion decision: blocked fine-line model not promoted to server or Flutter after QA review