Detection of diabetic retinopathy using computational model of human visual system

Fadafen, Masoud Khazaee; Mehrshad, Nasser; Razavi, Seyyed Mohammad

doi:10.4066/biomedicalresearch.29-18-551

Cited by 13 publications

(5 citation statements)

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“…Table 6 shows the comparison between proposed and existing method through methodology, method, performance and datasets used for validation. M. K. Fadafen [13] used Mathematical morphology which is the basic version of segmentation methodology.…”

Section: Resultsmentioning

confidence: 99%

“…This kind of analysis won't give any idea about DR grading and most of them stopped at the classification phase only. Even though our method has gained less AUC compared to [13], it was a full-pledged system that ends up with severity grading. Next, Pratt et al [33] employed a 13-layered CNN model for the grading of DR.…”

Section: Resultsmentioning

confidence: 99%

“…Fadafen et al [13] applied mathematical morphology over retinal images after the exclusion of OD and they tried to extract exudates. They used feature and edge based identification of blood vessels through three features namely direction, width and brightness.…”

Section: Literature Surveymentioning

confidence: 99%

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Retinal Image Lesions Assisted Diabetic Retinopathy Screening System Through Machine Learning

2022

IJIES

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This paper proposes an Automatic Screening System that identifies the symptoms of Diabetic Retinopathy from retinal fundus images and determines the severity. The entire system is of four-fold; the first phase pre-processes the retinal images and eliminates uneven illuminations. The second phase segments the major parts of images such as optic Disk and Blood Vasculature. Once the segmentation is completed, the residual part is called as lesion image and it is subjected to a hierarchical classification in the third phase. It is a two-step classification method which classifies the entire image into four regions namely Hard Exudates, Microaneursyms, Hemorrhages, cotton wool spots. For classification, three classifiers such as k-Nearest Neighbor, Gaussian Mixture Model and Support vector Machine are analyzed. Finally, the classified regions are processed through an objective function for severity grading. The novelty of this paper is a composite feature extraction method which extracts different and diverse features from lesion image. Since the lesions are very small and of different shapes, the proposed feature extraction method can provide more discriminant information for classifier. For experimental validation, a standard and publicly available DIARETDB1 dataset and the performance is measured through sensitivity, specificity, accuracy and area under curve. On an average, the proposed method gained accuracy, sensitivity, and specificity of 90.2 %, 95.2 % and 82.1 % respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Retinal Image Lesions Assisted Diabetic Retinopathy Screening System Through Machine Learning

2022

IJIES

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“…Other diagnostic models, such as support vector machines (SVM) have been also under great consideration as a bi-objective hybrid optimization algorithm to select the least number of significant features with the most classification accuracy and to decrease noise and data dimension in diagnosis [ 43 ]. There are also some algorithms for the diagnosis of retinopathy in diabetic patients based on the Human Visual System (HVS) model or type-2 fuzzy regression model [ 44 , 45 ]. It seems that performing more studies in this field can improve the capability of early diagnosis of diabetes and its complications.…”

Section: Discussionmentioning

confidence: 99%

Iran diabetes research study; knowledge discovery in diagnosis: a scoping review

Fana¹,

Ebrahimi²,

Esmaeili³

et al. 2021

J Diabetes Metab Disord

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Introduction The rising prevalence of diabetes shows high health and socio-economic burdens. Therefore, the development and evaluation of new diagnostic methods may improve the detection of disease and its complications in the early stages. This study aimed to analyze the scope of the studies related to diabetes diagnosis. Material and method Publications from January 2015 until December 2019 (5 years) were searched with keywords of (diabetes OR diabetic) AND (Iran) in Scopus and PubMed databases. All data were reviewed by two reviewers and the included publications were categorized based on the subjects, study design, and publication year. Results Based on the selected criteria, 103 articles were included. The highest number of publications was observed in 2019. The trend of publication was slightly increased during the study period (2015-2019). Case-control and cross-sectional studies were the most common type of study design used in the included documents. Publications in the field of diagnostic models, biomarkers, and biosensors from 2015 to 2019 showed an increasing trend compared to others subjects. Discussion and conclusion Studies about proper diabetes diagnostic procedures such as new diagnostic techniques, using diagnostic models, and evaluation of new diagnostic biomarkers in Iran are remarkably increased. However, more original and review studies are needed to improve scientific methods in the field of early detection of diabetes. Supplementary Information The online version contains supplementary material available at 10.1007/s40200-021-00843-x.

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“…Electrostatic discharge algorithm (EDA) is another similar algorithm developed by Bouchekara based on electrostatic discharge [343]. Ohm's Law has also been used in the Ohm's Law optimization (OLO) algorithm [344]. Kaveh et al have also used Coulomb's Law from electrostatics and the Newtonian Laws of Mechanics to develop the charged system search (CSS) [345].…”

Section: Other Algorithmsmentioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

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Detection of diabetic retinopathy using computational model of human visual system

Cited by 13 publications

References 0 publications

Retinal Image Lesions Assisted Diabetic Retinopathy Screening System Through Machine Learning

Retinal Image Lesions Assisted Diabetic Retinopathy Screening System Through Machine Learning

Iran diabetes research study; knowledge discovery in diagnosis: a scoping review

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

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