A Deep Learning Application for Psoriasis Detection

TL;DR

This study compares three CNN models for psoriasis detection, finding Inception v3 to be the most effective with high accuracy and F1-score, supporting clinical diagnosis.

Contribution

The paper provides a comparative analysis of CNN architectures for psoriasis classification, highlighting Inception v3's superior performance in this medical imaging task.

Findings

Inception v3 achieved 97.5% accuracy.

Model performance was validated on specialized skin image datasets.

The study supports using deep learning models for psoriasis diagnosis.

Abstract

In this paper a comparative study of the performance of three Convolutional Neural Network models, ResNet50, Inception v3 and VGG19 for classification of skin images with lesions affected by psoriasis is presented. The images used for training and validation of the models were obtained from specialized platforms. Some techniques were used to adjust the evaluation metrics of the neural networks. The results found suggest the model Inception v3 as a valuable tool for supporting the diagnosis of psoriasis. This is due to its satisfactory performance with respect to accuracy and F1-Score (97.5% ${\pm}$ 0.2).