Classification of Optical Coherence Tomography using Convolutional Neural Networks

被引：7

作者：

Saraiva, A. A. ^{[2
,6
]}

Santos, D. B. S. ^{[1
]}

Pedro, Pimentel ^{[1
]}

Moura Sousa, Jose Vigno ^{[1
]}

Fonseca Ferreira, N. M. ^{[3
,4
]}

Batista Neto, J. E. S. ^{[6
]}

Soares, Salviano ^{[3
]}

Valente, Antonio ^{[2
,5
]}

机构：

[1] UESPI Univ State Piaui, Piripiri, Brazil

[2] Univ Tras Os Montes & Alto Douro, Vila Real, Portugal

[3] Coimbra Polytech, ISEC, Coimbra, Portugal

[4] Polytech Inst Porto, Inst Engn, Knowledge Engn & Decis Support Res Ctr GECAD, Porto, Portugal

[5] INESC TEC Technol & Sci, Porto, Portugal

[6] Univ Sao Paulo, Sao Carlos, Brazil

来源：

PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 3: BIOINFORMATICS | 2020年

关键词：

OCT; CNN; Classification; K-fol; Labeled Optical Coherence Tomography; CHOROIDAL NEOVASCULARIZATION; SEGMENTATION; ANGIOGRAPHY; FEATURES; OUTCOMES; OCT;

D O I：

10.5220/0009091001680175

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This article describes a classification model of optical coherence tomography images using convolution neural network. The dataset used was the Labeled Optical Coherence Tomography provided by (Kermany et al., 2018) with a total of 84495 images, with 4 classes: normal, drusen, diabetic macular edema and choroidal neovascularization. To evaluate the generalization capacity of the models k-fold cross-validation was used. The classification models were shown to be efficient, and as a result an average accuracy of 94.35% was obtained.

引用

页码：168 / 175

页数：8

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