Machine learning and pattern classification in identification of indigenous retinal pathology

Herbert Jelinek; Anderson Rocha; Tiago Carvalho; Siome Goldenstein; Jacques Wainer

doi:10.1109/IEMBS.2011.6091471

Machine learning and pattern classification in identification of indigenous retinal pathology

Herbert Jelinek, Anderson Rocha, Tiago Carvalho, Siome Goldenstein, Jacques Wainer

Research output: Book chapter/Published conference paper › Conference paper › peer-review

23 Citations (Scopus)

4 Downloads (Pure)

Abstract

Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance.

Original language	English
Title of host publication	Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	5951-5954
Number of pages	4
DOIs	https://doi.org/10.1109/IEMBS.2011.6091471
Publication status	Published - 2011
Event	2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Boston Marriott Copley Place Hotel, Boston, United States Duration: 30 Aug 2011 → 03 Sept 2011

Conference

Conference	2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Country/Territory	United States
City	Boston
Period	30/08/11 → 03/09/11
Other	The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc.

UN SDGs

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

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10.1109/IEMBS.2011.6091471

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Jelinek, H., Rocha, A., Carvalho, T., Goldenstein, S., & Wainer, J. (2011). Machine learning and pattern classification in identification of indigenous retinal pathology. In Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5951-5954). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEMBS.2011.6091471

Jelinek, Herbert ; Rocha, Anderson ; Carvalho, Tiago et al. / Machine learning and pattern classification in identification of indigenous retinal pathology. Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States : IEEE, Institute of Electrical and Electronics Engineers, 2011. pp. 5951-5954

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abstract = "Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance.",

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Jelinek, H, Rocha, A, Carvalho, T, Goldenstein, S & Wainer, J 2011, Machine learning and pattern classification in identification of indigenous retinal pathology. in Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 5951-5954, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, Massachusetts, United States, 30/08/11. https://doi.org/10.1109/IEMBS.2011.6091471

Machine learning and pattern classification in identification of indigenous retinal pathology. / Jelinek, Herbert; Rocha, Anderson; Carvalho, Tiago et al.
Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States: IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 5951-5954.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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Jelinek H, Rocha A, Carvalho T, Goldenstein S, Wainer J. Machine learning and pattern classification in identification of indigenous retinal pathology. In Proceedings of the 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States: IEEE, Institute of Electrical and Electronics Engineers. 2011. p. 5951-5954 doi: 10.1109/IEMBS.2011.6091471

Machine learning and pattern classification in identification of indigenous retinal pathology

Abstract

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UN SDGs

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