Publicação

Deep learning in the identification of psoriatic skin lesions

Detalhes bibliográficos
Resumo:	Psoriasis is a dermatological lesion that manifests in several regions of the body. Its late diagnosis can generate the aggravation of the disease itself, as well as of the comorbidities associated with it. The proposed work presents a computational system for image classification in smartphones, through deep convolutional neural networks, to assist the process of diagnosis of psoriasis. The dataset and the classification algorithms used revealed that the classification of psoriasis lesions was most accurate with unsegmented and unprocessed images, indicating that deep learning networks are able to do a good feature selection. Smaller models have a lower accuracy, although they are more adequate for environments with power and memory restrictions, such as smartphones.
Autores principais:	Lima, Gabriel Lenin Silva
Outros Autores:	Pires, Carolina; Beuren, Arlete Teresinha; Lopes, Rui Pedro
Assunto:	Image processing Deep learning Psoriasis classification Mobile application
Ano:	2024
País:	Portugal
Tipo de documento:	comunicação em conferência
Tipo de acesso:	acesso restrito
Instituição associada:	Instituto Politécnico de Bragança
Idioma:	inglês
Origem:	Biblioteca Digital do IPB

Descrição
Resumo:	Psoriasis is a dermatological lesion that manifests in several regions of the body. Its late diagnosis can generate the aggravation of the disease itself, as well as of the comorbidities associated with it. The proposed work presents a computational system for image classification in smartphones, through deep convolutional neural networks, to assist the process of diagnosis of psoriasis. The dataset and the classification algorithms used revealed that the classification of psoriasis lesions was most accurate with unsegmented and unprocessed images, indicating that deep learning networks are able to do a good feature selection. Smaller models have a lower accuracy, although they are more adequate for environments with power and memory restrictions, such as smartphones.