Retinal Biometric for Securing JPEG-Codec Hardware IP Core for CE Systems

Chaurasia, Rahul; Sengupta, Anirban

doi:10.1109/tce.2023.3264669

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…The problem of identifying counterfeit or illegal intellectual property (IP) cores incorporated into consumer electronics systems was tackled in work in [37]. To secure JPEG compression-decompression (CODEC) hardware IP cores, the researchers suggested a novel retina biometric-based hardware security mechanism.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Jagadale Sachin Mohan

2023

IJRITCC

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The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Jagadale Sachin Mohan

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

Retinal Biometric for Securing JPEG-Codec Hardware IP Core for CE Systems

Cited by 1 publication

References 39 publications

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

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