Classification of color colposcopic images by neural networks

被引：0

作者：

Claude, I ^{[1
]}

Winzenrieth, R ^{[1
]}

Pouletaut, P ^{[1
]}

Boulanger, JC ^{[1
]}

机构：

[1] Univ Technol Compiegne, F-60206 Compiegne, France

来源：

CGIV'2002: FIRST EUROPEAN CONFERENCE ON COLOUR IN GRAPHICS, IMAGING, AND VISION, CONFERENCE PROCEEDINGS | 2002年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents colposcopic image classification based on color parameters used in a comparison study of different artificial neural networks and the k-nearest neighbors reference method. In this study, significant image data bases are used (617 images) from which two sets of parameters is extracted to characterize the attribute of color. More precisely, we select a set of color componants from color spaces based on data analysis and inner characteristics of different colposcopic image examinations. Experimental results show the feasibility of this study and the efficiency of the two sets of parameters since 90.9% of pink/red image set and 78% of brown/yellow image set have been correctly classified.

引用

页码：394 / 397

页数：4

共 50 条

[41] Classification of Ultrasound Kidney Images using PCA and Neural Networks
Attia, Mariam Wagih
Abou-Chadi, F. E. Z.
Moustafa, Hossam El-Din
Mekky, Nagham
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2015, 6 (04) : 53 - 57
[42] MULTISPECTRAL CLASSIFICATION OF LANDSAT-IMAGES USING NEURAL NETWORKS
BISCHOF, H
SCHNEIDER, W
PINZ, AJ
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1992, 30 (03): : 482 - 490
[43] Classification of Images Acquired with Colposcopy Using Artificial Neural Networks
Simoes, Priscyla W.
Izumi, Narjara B.
Casagrande, Ramon S.
Venson, Ramon
Veronezi, Carlos D.
Moretti, Gustavo P.
da Rocha, Edroaldo L.
Cechinel, Cristian
Ceretta, Luciane B.
Comunello, Eros
Martins, Paulo J.
Casagrande, Rogerio A.
Snoeyer, Maria L.
Manenti, Sandra A.
CANCER INFORMATICS, 2014, 13 : 119 - 124
[44] Classification of Cancer Microscopic Images via Convolutional Neural Networks
Khan, Mohammad Azam
Choo, Jaegul
ISBI 2019 C-NMC CHALLENGE: CLASSIFICATION IN CANCER CELL IMAGING, 2019, : 141 - 147
[45] Convolutional neural networks applied to classification of nanoparticles and nanotubes images
Quintero-Lopez, Luis A.
Caro-Gutierrez, Jesus
Gonzalez-Navarro, Felix F.
Curiel-Alvarez, Mario A.
Perez-Landeros, Oscar M.
Radnev-Nedev, Nicola
2023 MEXICAN INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE, ENC, 2024,
[46] Classification of breast thermal images using artificial neural networks
Jakubowska, T
Wiecek, B
Wysocki, M
Drews-Peszynski, C
Strzelecki, M
PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2004, 26 : 1155 - 1158
[47] Combined Convolutional and Recurrent Neural Networks for Hierarchical Classification of Images
Koo, Jaehoon
Klabjan, Diego
Utke, Jean
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 1354 - 1361
[48] Contextual dynamic neural networks learning in multispectral images classification
Solaiman, B
Mouchot, MC
Hillion, A
IGARSS '96 - 1996 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM: REMOTE SENSING FOR A SUSTAINABLE FUTURE, VOLS I - IV, 1996, : 523 - 525
[49] Classification of Images as Photographs or Paintings by Using Convolutional Neural Networks
Miguel Lopez-Rubio, Jose
Molina-Cabello, Miguel A.
Ramos-Jimenez, Gonzalo
Lopez-Rubio, Ezequiel
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2021, PT I, 2021, 12861 : 432 - 442
[50] Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
Pimkin, Artem
Makarchuk, Gleb
Kondratenko, Vladimir
Pisov, Maxim
Krivov, Egor
Belyaev, Mikhail
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2018), 2018, 10882 : 877 - 886

← 1 2 3 4 5 →