Abstract: Rosacea is a chronic skin disease affecting millions of people worldwide, characterized by redness and inflammatory lesions on the face. Given the need to improve early detection, this research aims to develop a mobile application using convolutional neural networks to improve the preliminary diagnosis of rosacea. For this purpose, increases in sensitivity, specificity and accuracy percentages were evaluated. The study was applied, with a quantitative approach and an experimental design, specifically pre-experimental. The study variable was the preliminary diagnosis of rosacea, and the sample consisted of 100 images: 50 from rosacea patients and 50 from healthy people. The technique used for data collection was observation. The results of the implementation of the mobile application showed an increase in sensitivity of 2.7%, specificity of 1.97% and accuracy of 0.10%. In conclusion, the use of the mobile application with convolutional neural networks improves the preliminary diagnosis of rosacea by optimizing the indicators evaluated.

Angie Fiorella Sapaico-Alberto and Rosalynn Ornella Flores-Castañeda, “Mobile Application Using Convolutional Neural Networks for Preliminary Diagnosis of Rosacea” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160637

@article{Sapaico-Alberto2025,
title = {Mobile Application Using Convolutional Neural Networks for Preliminary Diagnosis of Rosacea},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160637},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160637},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Angie Fiorella Sapaico-Alberto and Rosalynn Ornella Flores-Castañeda}
}