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

TL;DR

This study compares CNNs and Vision Transformers in classifying psoriasis lesions, finding ViTs, especially DaViT-B, outperform CNNs with higher accuracy and efficiency in medical image analysis.

Contribution

It provides a comparative analysis demonstrating the superior performance of Vision Transformers over CNNs in psoriasis image classification.

Findings

ViTs achieved higher F1-scores than CNNs.

DaViT-B model obtained an F1-score of 96.4%.

ViTs are effective for medical image classification.

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.