DermAI: Clinical dermatology acquisition through quality-driven image collection for AI classification in mobile
Abstrak
AI-based dermatology adoption remains limited by biased datasets, variable image quality, and limited validation. We introduce DermAI, a lightweight, smartphone-based application that enables real-time capture, annotation, and classification of skin lesions during routine consultations. Unlike prior dermoscopy-focused tools, DermAI performs on-device quality checks, and local model adaptation. The DermAI clinical dataset, encompasses a wide range of skin tones, ethinicity and source devices. In preliminary experiments, models trained on public datasets failed to generalize to our samples, while fine-tuning with local data improved performance. These results highlight the importance of standardized, diverse data collection aligned with healthcare needs and oriented to machine learning development.
Penulis (12)
Thales Bezerra
Emanoel Thyago
Kelvin Cunha
Rodrigo Abreu
Fábio Papais
Francisco Mauro
Natália Lopes
Érico Medeiros
Jéssica Guido
Shirley Cruz
Paulo Borba
Tsang Ing Ren
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓