Survey on Retinal OCT Image Preprocessing, Segmentation, and Deep Learning-Based Classification

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Year : 2024 | Volume :11 | Issue : 03 | Page : –
By
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Ranjitha Rajan,

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S.N Kumar,

  1. 1Research Scholar, Lincoln University College – Kota Bharu, Malaysia, 1Assistant Professor, Department of Electrical Communication Engineering, Amal Jyothi College of Engineering, Koovappally, Kerala, India
  2. Associate Professor, Department of Electronics and Electrical Engineering, Amal Jyothi College of Engineering, Koovappally, Kerala, India

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Optical Coherence Tomography (OCT) is a non-invasive technique that generates high-resolution, detailed cross-sectional images of biological tissues. By utilizing low-coherence interferometry, OCT enables visualization of tissue microstructure with micron-scale resolution, making it useful in various medical fields such as ophthalmology, cardiology, and dermatology. In ophthalmology, OCT is extensively used for diagnosing and monitoring retinal diseases like macular degeneration and diabetic retinopathy, allowing doctors to assess changes in tissue morphology over time. Moreover, OCT plays a crucial role in guiding surgical procedures, assessing treatment efficacy, and advancing our understanding of disease pathogenesis. Its ability to provide detailed, real-time images with minimal patient discomfort has made OCT a cornerstone technology in modern medical imaging, revolutionizing patient care and research practices. This survey comprehensively reviews the recent advances in preprocessing, segmentation, and deep learning-based classification of retinal OCT images, highlighting the latest techniques, challenges, and future directions for improving diagnostic accuracy and clinical applications.

Keywords: Optical Coherence Tomography (OCT), age-related macular degeneration (AMD), diabetic retinopathy (DR), Optical Coherence Tomography (OCT), convolutional neural networks (CNNs)

[This article belongs to Journal of Image Processing & Pattern Recognition Progress (joipprp)]

How to cite this article:
Ranjitha Rajan, S.N Kumar. Survey on Retinal OCT Image Preprocessing, Segmentation, and Deep Learning-Based Classification. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(03):-.
How to cite this URL:
Ranjitha Rajan, S.N Kumar. Survey on Retinal OCT Image Preprocessing, Segmentation, and Deep Learning-Based Classification. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 20/07/2024
Accepted 25/09/2024
Published 11/10/2024
522

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