Deep Learning in the Identification of Psoriatic Skin Lesions

被引:0
|
作者
Lima, Gabriel Silva [1 ,3 ]
Pires, Carolina [2 ]
Beuren, Arlete Teresinha [1 ]
Lopes, Rui Pedro [3 ,4 ]
机构
[1] Univ Tecnol Fed Parana, Santa Helena, Brazil
[2] Univ Porto, Inst Biomed Sci Abel Salazar, Porto, Portugal
[3] Inst Politecn Braganca, Res Ctr Digitalizat & Intelligent Robot CeDRI, Braganca, Portugal
[4] Inst Politecn Braganca, Lab Sustentabilidade & Tecnol Reg Montanha, Braganca, Portugal
来源
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I | 2024年 / 14469卷
关键词
image processing; deep learning; psoriasis classification; mobile application; NEURAL-NETWORKS; CLASSIFICATION;
D O I
10.1007/978-3-031-49018-7_22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:298 / 313
页数:16
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