Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers

• URL: http://arxiv.org/abs/2506.10119v1
• Date: Wed, 11 Jun 2025 19:00:32 GMT
• Title: Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers
• Authors: Natanael Lucena, Fábio S. da Silva, Ricardo Rios,
• Abstract summary: This paper presents a comparison of the performance of CNNs and ViTs in the task of multi-classifying images containing lesions of psoriasis and diseases similar to it.<n>The ViTs stood out for their superior performance with smaller models.<n>This article reinforces the potential of ViTs for medical image classification tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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