Detec\c{c}\~ao da Psor\'iase Utilizando Vis\~ao Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers

This paper presents a comparison of the performance of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in the task of multi-classifying images containing lesions of psoriasis and diseases similar to it. Models pre-trained on ImageNet were adapted to a specific data set. Both achieved high predictive metrics, but the ViTs stood out for their superior performance with smaller models. Dual Attention Vision Transformer-Base (DaViT-B) obtained the best results, with an f1-score of 96.4%, and is recommended as the most efficient architecture for automated psoriasis detection. This article reinforces the potential of ViTs for medical image classification tasks.

Natanael Lucena, Fábio S. da Silva, Ricardo Rios · 2025