Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning

被引：6

作者：

de Lucena, Daniel Vitor ^{[1
,2
]}

Soares, Anderson da Silva ^{[2
]}

Coelho, Clarimar Jose ^{[3
]}

Wastowski, Isabela Jube ^{[4
]}

Galvao Filho, Arlindo Rodrigues ^{[3
]}

机构：

[1] Inst Fed Educ Ciencias & Tecnol Goias, Luziania, Brazil

[2] Univ Fed Goias, Inst Informat, Goiania, Go, Brazil

[3] Pontificia Univ Catolica Goias, Escola Informat, Goiania, Go, Brazil

[4] Univ Estadual Goias, Posgrad Ciencias Aplicadas Prod Saude, Goiania, Go, Brazil

来源：

COMPUTATIONAL SCIENCE - ICCS 2020, PT III | 2020年 / 12139卷

关键词：

Short-Wave InfraRed; Hyperspectral Imaging; Deep learning; Skin lesions; Dysplastic Nevus; Melanoma; CLASSIFICATION; SKIN; MELANOMA;

D O I：

10.1007/978-3-030-50420-5_45

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We propose a new method for the analysis and classification of HSI images. The method uses deep learning to interpret the molecular vibrational behaviour of healthy and tumoral human epithelial tissue, based on data gathered via SWIR (short-wave infrared) spectroscopy. We analyzed samples of Melanoma, Dysplastic Nevus and healthy skin. Preliminary results show that human epithelial tissue is sensitive to SWIR to the point of making possible the differentiation between healthy and tumor tissues. We conclude that HSI-SWIR can be used to build new methods for tumor classification.

引用

页码：599 / 612

页数：14

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