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Learning representations through ensemble of fuzzy c-means for identification of retinal pathologies

Published: 17 October 2017 Publication History

Abstract

Retinal image analysis is employed to automate screening process through low-level feature extraction and classification. Supervised classification approaches are dependent on kernels or distance metrics to handle complex manifolds as they warp feature space for effective classification with less complex boundaries between classes. Proposed approach identifies control points (Voronoi diagram) by exploring the structures of class specific manifolds which constructs complex boundaries with piecewise linear nature. Such a framework has less number of hyperparameters to tweak resulting easy control and understanding of the system. The learning characteristics of the proposed algorithm has been depicted on toy and optical coherence tomography data set. It has illustrated effective performance in identification of retinal pathologies and compared against off-the-shelf classifiers with various parameters. Proposed algorithm is capable of accommodating unsupervised approaches other than Fuzzy C-Means reflecting its adaptability.

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cover image ACM Other conferences
IML '17: Proceedings of the 1st International Conference on Internet of Things and Machine Learning
October 2017
581 pages
ISBN:9781450352437
DOI:10.1145/3109761
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Author Tags

  1. fractals
  2. fuzzy logic
  3. pattern recognition
  4. voronoi

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