Mobile Application for Classifying Skin Imperfections Using Transfer Learning and Android Integration

Piero Felipa Juarez W.; Alejandro Rodríguez Quiroga D.; Gerardo Sanchez Marroquin G.; Alexander Cardernas Peralta G.; Huarote Zegarra R. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.193

Resumen

This paper presents the development of a mobile application for automated skin imperfection classification using deep learning techniques. The system integrates Google Colab for model training with Hugging Face's pre-trained models, implementing transfer learning to classify five distinct dermatological conditions: acne, rosacea, eczema, psoriasis, and atopic dermatitis. The trained model is deployed through an Android application developed in Kotlin, enabling real-time diagnosis from smartphone cameras. The implementation leverages cloud-based GPU resources for efficient training while maintaining a lightweight mobile interface for accessibility in resource-limited settings. Performance metrics demonstrate high accuracy in disease classification, with potential applications in early detection and telemedicine scenarios. The system architecture emphasizes scalability, user privacy, and clinical utility, providing a practical tool for preliminary dermatological assessment. Achieving an accuracy of 85%.

Instituciones: Universidad Privada del Norte