Retinal Image Quality Classification Using Neurobiological Models of the Human Visual System

Mahapatra, Dwarikanath

doi:10.17077/omia.1052

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…In summary, conventional methods for retinal vessel segmentation, despite their merits, are limited by their inability to provide distinct representations, making them vulnerable to interference from pathological regions. On the other hand, DL has proven to deliver highly distinct representations, making it a more fitting solution to address the challenges presented by electronic vision, including retinal vessel segmentation [141][142][143].…”

Section: Am J Biomed Sci and Resmentioning

confidence: 99%

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

2023

AJBSR

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The application of new techniques in eye imaging has revolutionized the process by increasing accuracy and efficiency levels considerably. An essential aspect of analyzing ocular images is the correct segmentation of retinal vessels. Deep learning algorithms represent an effective solution giving high accuracy rates for this task. In this article readers can find an extensive overview of several novel approaches built specifically to address this issue-among them being a groundbreaking deep learning architecture that applies multi-scale/multi-level Convolutional Neural Networks (CNNs) to obtain a comprehensive hierarchical representation of retinal vessels. Furthermore, advanced approaches are also discussed on detecting retinal vessels from Fluorescein Angiography (FA) images -with notable examples being one that combines cross modality transfer with human in the loop learning helping reduce manual labelling efforts significantly while enhancing both accuracy and performance metrics simultaneously. These stateof-the-art methods unravel critical insights into how deep learning methods could improve retinal vessel segmentation. The impact they could have on diagnosing and treating various eye related illnesses is also noteworthy.

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Section: Am J Biomed Sci and Resmentioning

confidence: 99%

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

2023

AJBSR

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“…Conversely, Mahapatra et al (2016) focused their efforts on creating algorithms tailored to evaluate the quality of retinal images. Addressing the shortcomings of earlier IQA algorithms, which were dependent on manually-engineered features, the team incorporated elements of the human visual system into their approach.…”

Section: State-of-the-artmentioning

confidence: 99%

Revolutionizing eye disease diagnosis with deep learning and machine learning

Adamopoulou M,

Makrynioti D,

Gklistis G

et al. 2023

World J. Adv. Res. Rev.

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Ocular medical imaging analysis has been transformed by the recent developments in Deep Learning and Machine Learning. These techniques have made it possible to uncover more detailed information about the eyes which ophthalmologists can utilize in their diagnoses. In this article we will discuss how artificial intelligence applications are helping improve ocular medical imaging. The use of AI technologies has led to a substantial increase in accuracy and efficiency when analyzing eye diseases. By staying up to date on the latest advancements in this field healthcare professionals and researchers can ensure that they are providing their patients with the most effective diagnoses and treatments available. This article provides essential insights into how AI powered methods are changing the face of ocular medical imaging making it an essential read for anyone invested in improving healthcare outcomes in this area.

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“…ALL-IDB is a public image database for ALL that has been studied widely. However, several researchers [93], [94] claim that hundreds of images are not enough to build a robust CNN, so more public data is still needed for ALL diagnosis. Besides, genomic information is also provided for ALL such as TARGET [87] and BioGPS [95].…”

Section: B: Acute Lymphoblastic Leukemia (All) Diagnosismentioning

confidence: 99%

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

et al. 2019

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Big medical data pose great challenges to life scientists, clinicians, computer scientists, and engineers. In this paper, a group of life scientists, clinicians, computer scientists, and engineers sit together to discuss several fundamental issues. First, what are the unique characteristics of big medical data different from those of the other domains? Second, what are the prioritized tasks in clinician research and practices utilizing big medical data? And do we have enough publicly available data sets for performing those tasks? Third, do the state-of-the-practice and state-of-the-art algorithms perform good jobs? Fourth, are there any benchmarks for measuring algorithms and systems for big medical data? Fifth, what are the performance gaps of the state-of-the-practice and state-of-the-art systems handling big medical data currently or in the future? Finally, but not least, are we, life scientists, clinicians, computer scientists, and engineers, ready for working together? We believe that answering the above-mentioned issues will help define and shape the landscape of big medical data.

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Retinal Image Quality Classification Using Neurobiological Models of the Human Visual System

Cited by 6 publications

References 10 publications

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

A Comprehensive Overview of Deep Learning Techniques for Retinal Vessel Segmentation

Revolutionizing eye disease diagnosis with deep learning and machine learning

Landscape of Big Medical Data: A Pragmatic Survey on Prioritized Tasks

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