Data fusion for multi-lesion diabetic retinopathy detection

Herbert F. Jelinek; Ramon Pires; Rafael Padilha; Siome Goldenstein; Jacques Wainer; Terry Bossomaier; Anderson Rocha

doi:10.1109/CBMS.2012.6266342

Data fusion for multi-lesion diabetic retinopathy detection

Herbert F. Jelinek, Ramon Pires, Rafael Padilha, Siome Goldenstein, Jacques Wainer, Terry Bossomaier, Anderson Rocha

Biomedical Engineering & Biotechnology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.

Original language	British English
Title of host publication	Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
DOIs	https://doi.org/10.1109/CBMS.2012.6266342
State	Published - 2012
Event	25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 - Rome, Italy Duration: 20 Jun 2012 → 22 Jun 2012

Publication series

Name	Proceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)	1063-7125

Conference

Conference	25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012
Country/Territory	Italy
City	Rome
Period	20/06/12 → 22/06/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CBMS.2012.6266342

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Jelinek, H. F., Pires, R., Padilha, R., Goldenstein, S., Wainer, J., Bossomaier, T., & Rocha, A. (2012). Data fusion for multi-lesion diabetic retinopathy detection. In Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 Article 6266342 (Proceedings - IEEE Symposium on Computer-Based Medical Systems). https://doi.org/10.1109/CBMS.2012.6266342

@inproceedings{72c5dc63731545539d6723dce98a0628,

title = "Data fusion for multi-lesion diabetic retinopathy detection",

abstract = "Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.",

author = "Jelinek, {Herbert F.} and Ramon Pires and Rafael Padilha and Siome Goldenstein and Jacques Wainer and Terry Bossomaier and Anderson Rocha",

year = "2012",

doi = "10.1109/CBMS.2012.6266342",

language = "British English",

isbn = "9781467320511",

series = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

booktitle = "Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012",

note = "25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012 ; Conference date: 20-06-2012 Through 22-06-2012",

}

Jelinek, HF, Pires, R, Padilha, R, Goldenstein, S, Wainer, J, Bossomaier, T & Rocha, A 2012, Data fusion for multi-lesion diabetic retinopathy detection. in Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012., 6266342, Proceedings - IEEE Symposium on Computer-Based Medical Systems, 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012, Rome, Italy, 20/06/12. https://doi.org/10.1109/CBMS.2012.6266342

Data fusion for multi-lesion diabetic retinopathy detection. / Jelinek, Herbert F.; Pires, Ramon; Padilha, Rafael et al.
Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012. 2012. 6266342 (Proceedings - IEEE Symposium on Computer-Based Medical Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data fusion for multi-lesion diabetic retinopathy detection

AU - Jelinek, Herbert F.

AU - Pires, Ramon

AU - Padilha, Rafael

AU - Goldenstein, Siome

AU - Wainer, Jacques

AU - Bossomaier, Terry

AU - Rocha, Anderson

PY - 2012

Y1 - 2012

N2 - Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.

AB - Screening of Diabetic Retinopathy (DR) with timely treatment prevents blindness. Several researchers have focused their work on the development of computer-aided lesion-specific detectors. Combining detectors is a complex task as frequently the detectors have different properties and constraints and are not designed under a unified framework. We extend our previous work for detecting DR lesions based on points of interest and visual words to include additional detectors for the most common DR lesions and investigate fusion techniques to combine different classifiers for classification of normal or signs of diabetic retinopathy. The combination methods show promising results and shed light on the possible advantages of combining complementary lesion detectors for the DR diagnosis problem.

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U2 - 10.1109/CBMS.2012.6266342

DO - 10.1109/CBMS.2012.6266342

M3 - Conference contribution

AN - SCOPUS:84867297982

SN - 9781467320511

T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems

BT - Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012

T2 - 25th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2012

Y2 - 20 June 2012 through 22 June 2012

ER -

Data fusion for multi-lesion diabetic retinopathy detection

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