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Machine Learning Based Diabetic Retinopathy Detection and Classification

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Abstract

Diabetic retinopathy is a common disease among people with diabetes. Early stage diagnosis and treatment of diabetic retinopathy are essential to prevent the progression of diabetic retinopathy to the point of irreversible effects on vision. The diagnosis of diabetic retinopathy currently relies on analyzing fundus images by experienced physicians. However, due to the current global imbalance in healthcare resources, lack of healthcare in many regions, and the fact that 80% of diabetic patients are from low-income or middle-income countries call for computer-aided diagnosis to provide urgently needed help for diabetic retinopathy patients. This chapter aims to discuss machine learning-based diabetic retinopathy diagnosis. The fundus imaging tools and the open-domain datasets that can be used for diabetic retinopathy-related research are introduced. A variety of methods for diagnosis based on machine learning are also introduced, including detection methods for exudate and microaneurysm, classification methods for diabetic retinopathy with different severity scales, and segmentation methods for optic disk and blood vessel. Thereafter, we discuss the application of machine learning models to real-life scenarios.

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Authors and Affiliations

  1. School of Big Data & Software Engineering, Chongqing University, Chongqing, China

    Fei Long & Jun Sang

  2. Department of Electrical Engineering and Computer Science, Texas A&M University – Kingsville, Kingsville, TX, USA

    Mohammad S. Alam

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  1. Fei Long
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  2. Jun Sang
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Correspondence to Mohammad S. Alam .

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  1. Department of Electronics and Instrumentation, University of Kashmir, Srinagar, Jammu and Kashmir, India

    Shabir A. Parah

  2. Aligarh Muslim University, Aligarh, India

    Nasir N. Hurrah

  3. Department of Electronics Engineering, Aligarh Muslim University, AMU Aligarh, India

    Ekram Khan

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Long, F., Sang, J., Alam, M.S. (2023). Machine Learning Based Diabetic Retinopathy Detection and Classification. In: Parah, S.A., Hurrah, N.N., Khan, E. (eds) Intelligent Multimedia Signal Processing for Smart Ecosystems. Springer, Cham. https://doi.org/10.1007/978-3-031-34873-0_5

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