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A Short Review on Cataract Detection and Classification Approaches Using Distinct Ophthalmic Imaging Modalities

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Big Data Analytics in Astronomy, Science, and Engineering (BDA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13830))

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Abstract

A cataract is one of the leading causes of visual impairment worldwide compared with other major age-related eye diseases, including blindness, such as diabetic retinopathy, age-related macular degeneration, trachoma, and glaucoma. Cloudiness in the lens of an eye leads to an increasingly blurred vision where genetics and aging are the leading cause of cataracts. In recent years, various researchers have shown an interest in developing state-of-the-art machine learning and deep learning techniques-based methods that work on distinct ophthalmic imaging modalities aiming to detect and prevent cataracts in the early stage. This survey highlights the advances in machine learning and deep learning state-of-the-art algorithms and techniques applied to cataract detection and classification using slit lamps, fundus retinal images, and digital camera images. In addition, this survey also provides insights into previous works along with the merits and demerits.

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Authors and Affiliations

  1. Ajay Kumar Garg Engineering College, Ghaziabad, India

    Aakash Garg, Jay Kant Pratap Singh Yadav & Sunita Yadav

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  1. Aakash Garg
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  2. Jay Kant Pratap Singh Yadav
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  3. Sunita Yadav
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Correspondence to Jay Kant Pratap Singh Yadav .

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Editors and Affiliations

  1. National Institute of Technology Delhi, New Delhi, India

    Shelly Sachdeva

  2. University of Aizu, Fukushima, Japan

    Yutaka Watanobe

  3. University of Aizu, Fukushima, Japan

    Subhash Bhalla

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Garg, A., Yadav, J.K.P.S., Yadav, S. (2023). A Short Review on Cataract Detection and Classification Approaches Using Distinct Ophthalmic Imaging Modalities. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big Data Analytics in Astronomy, Science, and Engineering. BDA 2022. Lecture Notes in Computer Science, vol 13830. Springer, Cham. https://doi.org/10.1007/978-3-031-28350-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-28350-5_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-28350-5

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