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Open Access
Article
Publication date: 6 December 2022

Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Pitipol Choopong, Thanongchai Siriapisith, Nattaporn Tesavibul, Nopasak Phasukkijwatana, Supalert Prakhunhungsit and Sutasinee Boonsopon

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It…

Abstract

Purpose

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

Design/methodology/approach

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

Findings

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

Originality/value

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Expert briefing
Publication date: 30 April 2019

Innovating in healthcare.

Article
Publication date: 1 March 2005

Jim Slattery

To evaluate various designs for a quality assurance system to detect and control human errors in a national screening programme for diabetic retinopathy.

Abstract

Purpose

To evaluate various designs for a quality assurance system to detect and control human errors in a national screening programme for diabetic retinopathy.

Design/methodology/approach

A computer simulation was performed of some possible ways of sampling the referral decisions made during grading and of different criteria for initiating more intensive QA investigations. The effectiveness of QA systems was assessed by the ability to detect a grader making occasional errors in referral.

Findings

Substantial QA sample sizes are needed to ensure against inappropriate failure to refer. Detection of a grader who failed to refer one in ten cases can be achieved with a probability of 0.58 using an annual sample size of 300 and 0.77 using a sample size of 500.

Originality/value

An unmasked verification of a sample of non‐referrals by a specialist is the most effective method of internal QA for the diabetic retinopathy screening programme. Preferential sampling of those with some degree of disease may improve the efficiency of the system.

Details

International Journal of Health Care Quality Assurance, vol. 18 no. 2
Type: Research Article
ISSN: 0952-6862

Keywords

Article
Publication date: 26 June 2021

Chinedum Ogbonnaya Eleazu, Aniza Abd Aziz, Tay Chuu Suen, Lam Chun-Hau, Chin Elynn, Chia Yi Hen, Ivan Ho Khor Ee, Lau Li Ren, Sabarisah Hashim and Mahaneem Mohamed

This study aims to design to assess the traditional, complementary and alternate medicine (TCAM) usage and its association with the quality of life (QOL) of Type 2 diabetic

Abstract

Purpose

This study aims to design to assess the traditional, complementary and alternate medicine (TCAM) usage and its association with the quality of life (QOL) of Type 2 diabetic patients in a tertiary hospital (Hospital Universiti Sains Malaysia) in Malaysia.

Design/methodology/approach

A total of 300 respondents included in this study were divided into the following two major categories: TCAM (34.33% of respondents) and non-TCAM users (65.67% of the respondents), respectively. The mean ages of the respondents were 59.3 ± 10.2 for the TCAM users and 57.7 ± 12.0 for the non-TCAM users.

Findings

A greater percentage of non-TCAM users reported poor control of diabetes (14.7%) and blood glucose (55.8%) compared with the TCAM users (9.7% and 48.5%, respectively). Further, the diabetic patients on TCAM reported lower rates of coma, stroke and kidney problems but higher rates of diabetic foot ulcers, heart diseases and retinopathy than the non-TCAM users. Additionally, the diabetic patients with TCAM usage had a significantly better physical (p = 0.02) and overall (p = 0.03) qualities of life compared to the non-TCAM users. However, psychological, social and environmental health did not show any significant difference.

Originality/value

The prevalence of TCAM usage among diabetic patients was lower than in other comparable studies. Diabetic patients on TCAM reported lower rates of coma, stroke and kidney problems but higher rates of diabetic foot ulcers, heart diseases and retinopathy than the non-TCAM users. Further, diabetes patients on TCAM reported better QOL compared to non-TCAM users especially in terms of physical health.

Details

Nutrition & Food Science , vol. 52 no. 1
Type: Research Article
ISSN: 0034-6659

Keywords

Article
Publication date: 22 March 2021

Naganagouda Patil, Preethi N. Patil and P.V. Rao

The abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental…

Abstract

Purpose

The abnormalities of glaucoma have high impact on deciding and representing the causes that effects severity of blindness in human beings. The simulation experimental results would help the ophthalmologist in diagnosing of glaucoma abnormality accurately. The significant effect of glaucoma has a huge impact on the quality of human life, and its growth rate in world population tremendously increases. Glaucoma is considered as second largest cause for the blindness in the world; hence identification of it marks the importance of its detection at the earliest.

Design/methodology/approach

The prime objective of the work proposed is to build up a human intervention free image preparing framework for glaucoma screening. The disc calculation is assessed on retinal image dataset called retinal Image for glaucoma Analysis. The proposed method briefs a novel optic disc division calculation depending on applying a level-set strategy on a confined optic disc image. In the instance of low quality image, a twofold level set is designed, in which the principal level set is viewed as restriction for the optic disc. To keep the veins from meddling with the level-set procedure, an inpainting strategy has been applied. Also a significant commitment is to include the varieties in notion adopted by the ophthalmologists in distinguishing the disc localization and diagnosing the glaucoma. Most of the past investigations are prepared and tested depending on just a single feature, which can be thought to be one-sided for the ophthalmologist.

Findings

In continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

Originality/value

In continuation, the correctness has been determined depending on the quantity of image that matched with the investigation pattern adopted by the ophthalmologist. The 175 retinal images were utilized to test the results of proposed work with the manual markings of ophthalmologists. The error-free calculation in marking the optic disc region and centroid was 98.95% in comparison with the existing result of 87.34%.

Details

International Journal of Intelligent Unmanned Systems, vol. 10 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 3 July 2020

Ambaji S. Jadhav, Pushpa B. Patil and Sunil Biradar

Diabetic retinopathy (DR) is a central root of blindness all over the world. Though DR is tough to diagnose in starting stages, and the detection procedure might be…

Abstract

Purpose

Diabetic retinopathy (DR) is a central root of blindness all over the world. Though DR is tough to diagnose in starting stages, and the detection procedure might be time-consuming even for qualified experts. Nowadays, intelligent disease detection techniques are extremely acceptable for progress analysis and recognition of various diseases. Therefore, a computer-aided diagnosis scheme based on intelligent learning approaches is intended to propose for diagnosing DR effectively using a benchmark dataset.

Design/methodology/approach

The proposed DR diagnostic procedure involves four main steps: (1) image pre-processing, (2) blood vessel segmentation, (3) feature extraction, and (4) classification. Initially, the retinal fundus image is taken for pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filter. In the next step, the blood vessel segmentation is carried out using a segmentation process with optimized gray-level thresholding. Once the blood vessels are extracted, feature extraction is done, using Local Binary Pattern (LBP), Texture Energy Measurement (TEM based on Laws of Texture Energy), and two entropy computations – Shanon's entropy, and Kapur's entropy. These collected features are subjected to a classifier called Neural Network (NN) with an optimized training algorithm. Both the gray-level thresholding and NN is enhanced by the Modified Levy Updated-Dragonfly Algorithm (MLU-DA), which operates to maximize the segmentation accuracy and to reduce the error difference between the predicted and actual outcomes of the NN. Finally, this classification error can correctly prove the efficiency of the proposed DR detection model.

Findings

The overall accuracy of the proposed MLU-DA was 16.6% superior to conventional classifiers, and the precision of the developed MLU-DA was 22% better than LM-NN, 16.6% better than PSO-NN, GWO-NN, and DA-NN. Finally, it is concluded that the implemented MLU-DA outperformed state-of-the-art algorithms in detecting DR.

Originality/value

This paper adopts the latest optimization algorithm called MLU-DA-Neural Network with optimal gray-level thresholding for detecting diabetic retinopathy disease. This is the first work utilizes MLU-DA-based Neural Network for computer-aided Diabetic Retinopathy diagnosis.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 7 February 2021

Sengathir Janakiraman, Deva Priya M., Christy Jeba Malar A., Karthick S. and Anitha Rajakumari P.

The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or…

Abstract

Purpose

The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss.

Design/methodology/approach

The proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 × 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process.

Findings

The results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity.

Research limitations/implications

DRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process.

Practical implications

The IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis.

Social implications

This proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture.

Originality/value

The performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Open Access
Article
Publication date: 7 October 2021

Enas M.F. El Houby

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision…

1246

Abstract

Purpose

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.

Design/methodology/approach

In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.

Findings

By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.

Originality/value

In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 30 January 2007

Randhir Chavan, Arijit Mitra and Maurice Headon

The purpose of this paper is to assess the benefits of introducing a laser room poster in improving delivery of laser pan retinal photocoagulation (PRP) in treatment of…

268

Abstract

Purpose

The purpose of this paper is to assess the benefits of introducing a laser room poster in improving delivery of laser pan retinal photocoagulation (PRP) in treatment of proliferative diabetic retinopathy (PDR).

Design/methodology/approach

An audit was carried out to describe the practice prior to introducing laser room poster. A reaudit was conducted after its introduction to evaluate if the practice had improved.

Findings

There was a 20 per cent increase (92 per cent in reaudit versus 72 per cent in audit) in total number of laser burns between 1,000‐2,500 burns during first session. In a completed PRP, an increase of 23 per cent was recorded (45 per cent versus 22 per cent) in total number of laser burns between 3,000‐6,000 burns. Retinal area ablated less than recommended by ETDRS fell by 21 per cent (18 versus 39). Not surprisingly the percentage of patients requiring re‐treatment after completed PRP fell from 30 per cent to 21 per cent, indicating an overall improvement in PRP treatment results.

Originality/value

Future strategies for improving clinical effectiveness can be based on experience gained from this project. Laser room poster can be an important tool in ophthalmology departments in the management of PDR.

Details

Clinical Governance: An International Journal, vol. 12 no. 1
Type: Research Article
ISSN: 1477-7274

Keywords

Content available

Abstract

Details

Clinical Governance: An International Journal, vol. 15 no. 2
Type: Research Article
ISSN: 1477-7274

1 – 10 of 119