Development of a Mobile Application for Dermatological Diagnosis Using Image Recognition: The DermAware Case Study
Journal
Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
ISSN
1860-949X
1860-9503
Publisher
Springer Nature Switzerland
Date Issued
2025
Author(s)
Cedillo-Maldonado, Luis
Sigüenza-Noriega, Iñaki
Miranda-Mateos, Sara Rocio
Reinoso-Fuentes, Lorenzo
Pérez-Aguirre, Mauricio
Escobar-Castillejos, Daisy
Type
text::book::book part
Abstract
This chapter discusses the development of DermAware, an iOS mobile application intended for assisting dermatological diagnosis through image recognition and machine learning. DermAware aims to serve as a supplementary resource for specialized medical professionals and patients, aiding the early identification and supervision of dermatological illnesses. The application was developed with SwiftUI for the front end and Django for the back end, providing scalability and secure data management. The application incorporates multiple components designed to enhance its usability. A real-time messaging module was designed to facilitate direct communication among users for prompt consultation scheduling. Health tracking functionalities, supported by Apple’s HealthKit, enable the collection and monitoring of patient data. The application integrates patient history management, allowing doctors to review previous assessments and track disease development conveniently. Ultimately, an authentication system guarantees data confidentiality and adherence to regulations. DermAware uses Apple’s CoreML framework alongside the ResNet50 convolutional neural network model to classify skin diseases. The system was trained using publicly accessible dermatological datasets, achieving an accuracy of 85% for detecting various skin conditions, including melanoma. The chapter finishes by addressing the technical challenges faced during development, evaluating potential enhancements, and discussing future developments in the field. These factors highlight the significance of AI-driven applications for improving medical diagnostics and healthcare accessibility. ©The authors ©Springer.
License
Acceso Restringido
How to cite
Cedillo-Maldonado, L. et al. (2026). Development of a Mobile Application for Dermatological Diagnosis Using Image Recognition: The DermAware Case Study. In: Moya-Albor, E., Ponce, H., Brieva, J., Gomez-Coronel, S.L., Torres, D.R. (eds) Machine Learning Methods in Biomedical Field. Studies in Computational Intelligence, vol 1218. Springer, Cham. https://doi.org/10.1007/978-3-031-96328-5_2