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

Source: [Detec\\c{c}\\~ao da Psor\\'iase Utilizando Vis\\~ao Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers](<https://www.sota2.com/research/paper/68fa79531e209131cea7d162>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:55:51.332Z

Authors: Natanael Lucena, F\\'abio S. da Silva, Ricardo Rios

Published: 2025-06-11T19:00:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.10119>)

[PDF](<https://arxiv.org/pdf/2506.10119.pdf>)