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Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review

  • Sarni Suhaila Rahim
  • Vasile Palade
  • Andreas HolzingerEmail author
Chapter
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12090)

Abstract

An effective automatic diagnosis and grading of diabetic retinopathy would be very useful in the management of the diabetic retinopathy within the national health system. The detection of the presence of diabetic retinopathy features in the eyes is a challenging problem. Therefore, highly efficient and accurate image processing and machine learning techniques must be used in order to produce an effective automatic diagnosis of diabetic retinopathy. This chapter presents an up-to-date review on diabetic retinopathy detection systems that implement a variety of image processing techniques, including fuzzy image processing, along various machine learning techniques used for feature extraction and classification. Some background on diabetic retinopathy, with a focus on the diabetic retinopathy features and the diabetic retinopathy screening process, is included for better understanding. The chapter also highlights the available public databases, containing eye fundus images, which can be currently used in the diabetic retinopathy research. As the development of an automatic diabetic retinopathy screening system is a very challenging task, some of these challenges together with a discussion pertaining the automatic diabetic retinopathy screening are also presented in this chapter.

Keywords

Diabetic retinopathy Eye screening Fundus images Image processing Machine learning 

Notes

Acknowledgements

This study is a part of postdoctoral research currently being carried out at the Faculty of Engineering, Environment and Computing, Coventry University, United Kingdom. The deepest gratitude and thanks go to the Universiti Teknikal Malaysia Melaka (UTeM) for sponsoring this postdoctoral research.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sarni Suhaila Rahim
    • 1
    • 2
  • Vasile Palade
    • 1
  • Andreas Holzinger
    • 3
    Email author
  1. 1.Faculty of Engineering, Environment and ComputingCoventry UniversityCoventryUK
  2. 2.Faculty of Information and Communication TechnologyUniversiti Teknikal Malaysia MelakaMelakaMalaysia
  3. 3.Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria

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