Diabetic Retinopathy Detection Using Transfer and Reinforcement Learning with Effective Image Preprocessing and Data Augmentation Techniques

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Tariq, Maria, Palade, Vasile, Ma, YingLiang ORCID: https://orcid.org/0000-0001-5770-5843 and Altahhan, Abdulrahman (2023) Diabetic Retinopathy Detection Using Transfer and Reinforcement Learning with Effective Image Preprocessing and Data Augmentation Techniques. In: Fusion of Machine Learning Paradigms. Intelligent Systems Reference Library, 236 . Springer, pp. 33-61. ISBN 978-3-031-22370-9

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Abstract

Diabetic retinopathy is the consequence of advanced stages of diabetes, which can ultimately lead to permanent blindness. An early detection of diabetic retinopathy is extremely important to avoid blindness and to recover from it as soon as possible. This chapter discusses the application of recent deep and transfer learning models for medical image analysis, with the focus on diabetic retinopathy detection. The chapter presents an extensive discussion on the publicly available datasets with diabetic retinopathy images, and the Kaggle dataset is used for training and testing of our proposed model. The main challenges to handle noisy and not large enough datasets are discussed in this chapter as well, where image preprocessing techniques and data augmentation play a significant role. An extensive overview of recent data augmentation techniques is also given to tackle the problem of imbalanced nature of diabetic retinopathy datasets. The proposed model integrates deep learning and reinforcement learning to perform detection and imbalanced classification on the Kaggle dataset.

Item Type:	Book Section
Uncontrolled Keywords:	deep learning,diabetic retinopathy,reinforcement learning,transfer learning,information systems and management,library and information sciences,computer science(all),sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/1800/1802
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?... https://link.springer.com/chapter/10.100...
Depositing User:	LivePure Connector
Date Deposited:	14 Feb 2023 10:30
Last Modified:	10 Dec 2024 01:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/91124
DOI:	10.1007/978-3-031-22371-6_3

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