Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features

Abbas, Qaisar; Fondón, Irene; Sarmiento, Auxiliadora; Jiménez, Soledad Ballesteros; Alemany, P.

doi:10.1007/s11517-017-1638-6

Cited by 136 publications

(91 citation statements)

References 40 publications

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“…Implementing AI into DR screening and surveillance services may assist in increasing efficiency in DR management. [58][59][60] This has culminated in a few significant AI MLCs being created that demonstrate classification results similar to trained graders and ophthalmologists. In these studies, MLCs have already proven their capacity to recognize and classify DR from normal images.…”

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

“…In these studies, MLCs have already proven their capacity to recognize and classify DR from normal images. [58][59][60] This has culminated in a few significant AI MLCs being created that demonstrate classification results similar to trained graders and ophthalmologists. 3,38,61 These studies all used large datasets to create the MLCs.…”

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

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Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty

Mackey

Hewitt

2018

Clinical Exper Ophthalmology

144

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Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and treatment pathways for many eye conditions such as corneal ectasias, glaucoma, age-related macular degeneration and diabetic retinopathy. However, given that AI has primarily been driven as a computer science, its concepts and terminology are unfamiliar to many medical professionals. Important key terms such as machine learning and deep learning are often misunderstood and incorrectly used interchangeably. This article presents an overview of AI and new developments relevant to ophthalmology.

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Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

Section: Diagnosing Diabetic Retinopathymentioning

confidence: 99%

Current state and future prospects of artificial intelligence in ophthalmology: a review

Hogarty

Mackey

Hewitt

2018

Clinical Exper Ophthalmology

144

View full text Add to dashboard Cite

show abstract

“…About 90% of diabetic patients across the globe have T2D diabetes, which is stimulated by the resistance of insulin. Diabetic retinopathy (DR) is one of the significant ramifications of DM and creates a progressive visual illness in the adult population characterized by the deterioration of tinny retinal vessels [4][5][6]. However, early screening and treatment through computer-aided diagnostic (CAD) programs can assist patients to prevent complete blindness occurring.…”

Section: Introductionmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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“…The studies are classified into detection [9,10], recognition [11], classification [12], and segmentation [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Performance on Medical Image, Data and Signals

Erdoğmuş

2019

Sakarya University Journal of Computer and Information Sciences

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In this study, the recent medical studies with deep learning between 2009-2019 have been researched for observing the performance of deep learning on medical images, data and signal. Recent studies attained from Web of Science have been evaluated and selected according to the citation numbers. Studies have been listed as a table, according to the publication year, deep network structure, database used training and testing, evaluation metric and results. The studies have also been classified into the organs and the types of important diagnosis. The results have shown that the deep learning network structures, applied on fundus images, have attained nearly %99 percent accuracy. Although most of the studies between the range, made by Radiology and Nuclear Medicine Imaging, the accuracy of the results are 80-90% range. The current studies especially focus on automatic detection or classification of the tumor as benign or malign. Studies are mostly on medical CT, ultrasound, radiography and MRI images. This results show that computer aided medical diagnosis systems will be used in a very near future with fully performance.

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Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features

Cited by 136 publications

References 40 publications

Current state and future prospects of artificial intelligence in ophthalmology: a review

Current state and future prospects of artificial intelligence in ophthalmology: a review

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Deep Learning Performance on Medical Image, Data and Signals

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