An Intelligent Particle Swarm Optimization with Convolutional Neural Network for Diabetic Retinopathy Classification Model

Jayanthi, J.; Jayasankar, T.; Krishnaraj, N.; Prakash, N. B.; Britto, A. Sagai Francis; Kumar, Kundan

doi:10.1166/jmihi.2021.3362

Cited by 38 publications

(15 citation statements)

References 12 publications

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“…Various standard metrics like that as f1 measure, recall, accuracy and precision were computed, as well as segmentation measures such as DSC, JSC, and time period. The performance of six different classifiers, SVM-GSO [26], PSO-CNN [27], CNN [28], DCNN-EMFO [29], MACO-CNN [30], and the suggested IGWO-FFOCNN, is shown in the figure9. It shows that the suggested IGWO-FFOCNN has a higher TNR outcome percentage and a lower FPR, FNR.…”

Section: Resultsmentioning

confidence: 99%

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Vijayalakshmi¹,

Kumar²

2022

ijhs

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Diabetic Retinopathy is a retinal eye disease that affects people with Diabetes Mellitus. DM is metabolic disorder and it is caused by the high glucose level in blood. This leads to eye deficiency called Diabetic Retinopathy (DR). In this paper, we proposed iGWO algorithm for the detection and diagnosis of DR stages and Region growing technique is used for segmentation of the images. Various preprocessing techniques are followed for the better enhancement of the images. An improved Grey Wolf Optimization (iGWO) algorithm are proposed to obtain the global optimum. Convnets are utilized for DR categorization. The iGWO-FFOCNN model is compared with existing technologies like SVM-GSO (Support Vector Machines – Glowworm Swam Optimization), PSO-CNN (Particle Swarm Optimization-Convolutional Neural Networks), CNN (Convolutional Neural Networks), DCNN-EMFO (Deep Convolutional Neural Networks- Enhanced Moth Flame Optimization) and MACO-CNN (Modified Ant Colony Optimization–CNN). The APTOS DR Dataset utilized for the proposed metodology. The effort is made for identifying the DR stages by using iGWO. Finally, the results confirm that iGWO-FFOCNN technique yields good performance than compared to the existing techniques in relation to F-measure, precision, recall and also in DSC (Dice Similarity Coefficient), JSC (Jaccard Similarity Coefficient) and time period.

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Section: Resultsmentioning

confidence: 99%

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Vijayalakshmi¹,

Kumar²

2022

ijhs

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“…e PSO-CNN model is simulated using a benchmark DR database, and the experimental results show that the PSO-CNN model outperforms all other approaches by a substantial margin [39] (Table 1).…”

Section: Literature Reviewmentioning

confidence: 99%

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Valizadeh

Ghoushchi

Ranjbarzadeh

et al. 2021

Computational Intelligence and Neuroscience

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Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients’ fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.

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“…The profile of epithelial thickness indicated a thinner region of epithelium temporally, in-line with surface apex posterior, that recommended Keratoconus diagnosis [11][12][13][14]. However, the epithelium classification function had not classified it as keratoconus.…”

Section: Related Workmentioning

confidence: 99%

Improvement in Keratoconus Diagnosis Using Morpho-Geometric Variables With RNN Networks

Kanimozhi¹

2021

ITII

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There is an eye disease called Keratoconus (KC) which has potential to cause visual acuity loss; hence, it can be considered as disability due to its severity. There are some limitations in current method in detecting cornea region’s boarder edge. Primary objective for the paper need to identify the structural description of disease’ asymmetry with the help of Morpho-geometric parameters relates with the keratoconous eyes along by means of slight visual control. It also includes the application of Recurrent Neural Network (RNN) analysis which is sort of Neural Network in which previous step’s output are sent to present step as an input. In order to determine most prominent correlation, Stepwise Discriminant Function Analysis is used in analyzing output. The Prominent correlation was identified between thinnest point in the anterior deviation and thinnest point in the posterior deviations of minor keratoconic cases. MATLAB R2014 software is used to implement the framework and analyses of simulation results were performed.

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An Intelligent Particle Swarm Optimization with Convolutional Neural Network for Diabetic Retinopathy Classification Model

Cited by 38 publications

References 12 publications

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

improved grey wolf optimization algorithm (iGWO) for the detection of diabetic retinopathy using convnets and region based segmentation techniques

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Improvement in Keratoconus Diagnosis Using Morpho-Geometric Variables With RNN Networks

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