Affiliation:
1. Technical Informatics College of Akre, Duhok Polytechnic University, Duhok, Iraq
Abstract
Abstract:
Information-based image processing and computer vision methods are utilized in several
healthcare organizations to diagnose diseases. The irregularities in the visual system are identified over
fundus images with a fundus camera. Among ophthalmology diseases, glaucoma is the most common
case leading to neurodegenerative illness. The unsuitable fluid pressure inside the eye within the visual
system is described as the major cause of those diseases. Glaucoma has no symptoms in the early stages,
and if it is not treated, it may result in total blindness. Diagnosing glaucoma at an early stage may
prevent permanent blindness. Manual inspection of the human eye may be a solution, but it depends on
the skills of the individuals involved. The diagnosis of glaucoma by applying a consolidation of computer
vision, artificial intelligence, and image processing can aid in the prevention and detection of
those diseases. In this review article, we aim to introduce numerous approaches based on peripapillary
atrophy segmentation and classification that can detect these diseases, as well as details regarding the
publicly available image benchmarks, datasets, and measurement of performance. The review article
highlights the research carried out on numerous available study models that objectively diagnose glaucoma
via peripapillary atrophy from the lowest level of feature extraction to the current direction based
on deep learning. The advantages and disadvantages of each method are addressed in detail, and tabular
descriptions are included to highlight the results of each category. Moreover, the frameworks of
each approach and fundus image datasets are provided. Our study would help in providing possible
future work directions to diagnose glaucoma.
Publisher
Bentham Science Publishers Ltd.
Subject
Radiology, Nuclear Medicine and imaging
Cited by
1 articles.
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