Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues

Ishtiaq, Uzair; Kareem, Sameem Abdul; Abdullah, Erma Rahayu Mohd Faizal; Mujtaba, Ghulam; Jahangir, Rashid; Ghafoor, Hafiz Yasir

doi:10.1007/s11042-018-7044-8

Cited by 64 publications

(19 citation statements)

References 90 publications

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“…Computer-aided design and analysis are improved by augmenting machine learning algorithms and processing techniques to detect detection better. Convolution neural network (CNN) is one of the well-known machine learning techniques adapted for image processing diversely (Ishtiaq et al 2019). The hidden convolutional layers are responsible for validating the conditions and satisfying the constraints in handling differential inputs.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Shanthini

Manogaran

Vadivu

et al. 2021

J Ambient Intell Human Comput

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Diabetic retinopathy (DR) syndrome affects the vision of the eyes by damaging the blood vessels. Fore-hand detection and prevention of this syndrome are most significant as it results in vision blindness. Diagnosis and procedural analysis of this syndrome with modern healthcare science and technology are aided through artificial intelligence and processing units. In this article, a threshold segmentation based DR detection method is introduced. This method is keen is classifying the foreground and background of the input retinal image and processing through pixel-based segmentation. The process of assessing the layers is augmented using a two-layer convolutional neural network (CNN) that mitigates the false positives during classification. This process is sequential in determining the precise detection of the infected region of the retina. Besides, the segment-based CNN (S-CNN) handles the flaw in diagnosis through two-hidden layers for differentiating the threshold and normalized conditions based on classification. The proposed method is reliable in achieving better accuracy of detection, sensitivity, and true positives.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Shanthini

Manogaran

Vadivu

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

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“…The basic reason for the progression of this disease is lack of enough oxygen being received by the retina [29]. The advancement of DR is mainly denoted by four stages in medical terms namely: mild non-proliferative retinopathy, moderate non-proliferative retinopathy, severe nonproliferative retinopathy, and proliferative diabetic retinopathy [49] as shown in figure 5. As compared to early stages like mild, moderate non-proliferative retinopathy, later stages are the more severe form in which new fragile blood vessels are growing.…”

Section: Diabetic Retinopathy (Dr)mentioning

confidence: 99%

“…List of Eye Disease and their sign and symptoms[28][29] Mucus production in or around the eyes Further, one of the leading cause of blindness in today's most of the developing countries or industrialized world is purely dominant by the DR specially in the working adults[30]. DR advancement basically denoted by four stages in medical term namely:• Mild non-proliferative retinopathy Moderate non-proliferative retinopathy • Severe non-proliferative retinopathy • Proliferative diabetic retinopathy[31] Diabetic Macular Edema (DME) is the another most widespread critical form of blindness and almost develops in half of the people having DR…”

mentioning

confidence: 99%

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Kumar¹,

Kumar²,

Gupta³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment's it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less. OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc. METHODS: AI and Machine Learning Technique are used to conduct this research for analysis. RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology. CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.

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“…Mookiah et al [13], reviewed computer aided DR detection studies, which are largely DR lesion based. Another author, Ishtiaq et al [14], reviewed comprehensive DR detection methods from 2013 to 2018 but their review lacked studies from 2019 to 2020. Hagiwara et al [15], reviewed an article for the computer aided diagnosis of Gl using fundus images.…”

Section: A Motivationmentioning

confidence: 99%

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

et al. 2020

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Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This paper presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

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Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues

Cited by 64 publications

References 90 publications

RETRACTED ARTICLE: Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

RETRACTED ARTICLE: Threshold segmentation based multi-layer analysis for detecting diabetic retinopathy using convolution neural network

Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

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