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Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier

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Abstract

Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55 %.

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Acknowledgement

The project described was supported in part by Grant Number P20RR016456 from the National Center for Research Resources (NCRR). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCRR or the National Institutes of Health (NIH).

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Authors and Affiliations

  1. Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348, Ruston, LA, 71272, USA

    Sulaimon Ibrahim, Pradeep Chowriappa, Sumeet Dua & Hatwib Mugasa

  2. Department of Electronics & Computer Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore, 599489, Singapore

    U. Rajendra Acharya

  3. Department of Electronics & Communications, MIT Manipal, Manipal, 576104, India

    Kevin Noronha

  4. Department of Ophthalmology, KMC Manipal, Manipal, 576104, India

    Sulatha Bhandary

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  1. Sulaimon Ibrahim
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  2. Pradeep Chowriappa
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  3. Sumeet Dua
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  4. U. Rajendra Acharya
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  5. Kevin Noronha
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  6. Sulatha Bhandary
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  7. Hatwib Mugasa
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Correspondence to Sumeet Dua.

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Ibrahim, S., Chowriappa, P., Dua, S. et al. Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. Med Biol Eng Comput 53, 1345–1360 (2015). https://doi.org/10.1007/s11517-015-1329-0

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  • DOI: https://doi.org/10.1007/s11517-015-1329-0

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