Open AccessAutomatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning
Author(s) -
J. C. R. Souza,
Tiago de Oliveira,
Claudemir Casa,
André Roberto Ortoncelli
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2020.13499
Subject(s) - computer science , artificial intelligence , transfer of learning , convolutional neural network , butterfly , point (geometry) , rash , deep learning , malar rash , architecture , computer vision , pattern recognition (psychology) , dermatology , mathematics , medicine , geometry , finance , biology , antibody , immunology , economics , anti nuclear antibody , autoantibody , art , visual arts
This work presents an approach to the automatic detection of Butterfly Malar Rash (BMR) in images. BMR is a Lupus symptom characterized by a reddish facial rash that appears symmetrically in the cheeks and the back of the nose. The proposed approach is based on Transfer Learning, a popular approach in Deep Learning that consists in the use of pre-trained models as the starting point for computer vision and natural language processing tasks. To perform the experiments, a database was created with images manually collected from the Instagram social network, searching for images with #butterflyrash. We evaluated the proposed approach with eight Convolutional Neural Networks (CNN) architecture. The experimental results are good results, with a precision of up to 0.957.