Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm

Tamim, Nasser; Elshrkawey, Mohamed; Nassar, Hamed

doi:10.3390/app11136178

Cited by 12 publications

(3 citation statements)

References 43 publications

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“…The image annotation is done initially to remove noisy artifacts in DFI. A comprehensive review of semantic fundus image segmentation [4] was done to analyze the masking and segmentation processes in various pretrained network architectures. The author contributed the clinical dataset values in a single array system where all the fundus images are segmented in the model and compared with other datasets and clinical results.…”

Section: Related Workmentioning

confidence: 99%

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

Shamseera KK, Selvavinayaki K

2023

tjjpt

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To propose a bio-inspired multi-variant feature extraction technique in retinal fundus images to improve glaucoma detection for easy diagnosis. To improve the classification accuracy of glaucoma disease, a machine learning approach is employed based on the features extracted in RFI. Methods: The Fish School Search MOFS (FSS-MOFS) bio-inspired technique is employed to extract the multivariable features from digital RFI and spot the glaucoma variations to maximize the discrimination power and minimize the dimensionality. The Binary Robust Independent Elementary Feature (BRIEF-LD) technique is employed to identify the latent spots in RFI. The PAPILA dataset is utilized for this research study, which includes the clinical records of 244 patients and 488 images of the right and left eye of all the patients under 2 categories (male and female) with results in healthy, glaucoma, and suspect categories. In order to perform deep pixel identification and RFI segmentation, the Densenet-121 architecture is used along with a deep-lab model to incarcerate the built-in fine points of RFI with the help of dilated convolutions. The MATLAB tool is used to assess the suggested bio-inspired technique, and comparative analysis is done against the prevailing glaucoma detection and feature extraction methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. Findings: The suggested FSS-MOFS multi-variant feature extraction technique outperforms the prevailing ML methods such as I-SVM, RFSO, SE-GSO, VGG-Net, and DBN classifiers. The result of the comparative study shows 94% accuracy, 96% sensitivity, 97% specificity, 96% F-measure, 96.8% Precision, 97% recall, 95% detection speed, 92.06% TPR, 92.15% TNR, and 8% FPR & FNR. Novelty: The promising result of the FSS-MOFS method boosts the accuracy level of glaucoma detection and classification compared to other classification techniques. The suggested model helps the ophthalmologists identify and diagnose glaucoma in a robust way. FSS-MOFS overcomes the shortcomings of prevailing feature extraction methods.

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Section: Related Workmentioning

confidence: 99%

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

Shamseera KK, Selvavinayaki K

2023

tjjpt

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“…Table 2 and Figure 10 offer an extensive comparative study of the AI‐ODCNN model on the ARIA dataset (Chea & Nam, 2021; Liu et al, 2019; Tamim et al, 2021). The results indicated that the ResNet‐152 model has shown ineffectual classification results.…”

Section: Performance Validationmentioning

confidence: 99%

Artifical intelligence with optimal deep learning enabled automated retinal fundus image classification model

Gupta¹,

Choubey²,

Choubey³

2022

Expert Systems

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Diabetic retinopathy (DR) and age related macular degeneration (AMD) becomes widespread microvascular illness among diabetic patients. Traditional retinal fundus image classification requires visual inspection by the professionals, which is time consuming and requires expert's knowledge. Earlier identification of retinal diseases is essential to delay or avoid vision deterioration and vision loss. The recently developed artificial intelligence (AI) and deep learning (DL) models can be employed for accurate retinal image classification. With this motivation, this study designs a new artificial intelligence with optimal deep convolutional neural network (AI-ODCNN) technique for retinal fundus image classification. Primarily, the proposed model uses the Gaussian Blur based noise removal and contrast enhancement technique (CLAHE) based contrast enhancement technique to pre-process the retinal fundus image. In addition, morphology and contour based image segmentation is performed.Moreover, the deep CNN with RMSProp Optimizer is employed for retinal fundus image classification. A wide range of simulations was performed on the automated retinal image analysis and structured analysis of the retina and the outcomes are examined with respect to various measures. The simulation outcomes ensured the better performance of the proposed approach related to other recent algorithms with maximum accuracy of 96.47%.

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“…Retinal diseases include Choroidal Neovascularization (CNV), Age-Related Macular Degeneration (AMD), Diabetic Macula Edema (DME), glaucoma, Drusen and diabetic retinopathy (DR) [3]. Tamim et al [4] predicted that the number of people with glaucoma will peak to 111.8 million people by 2040. According to Chelaramani et al in [5] in 2015, 415 million people were suffering from diabetes, of which 145 million had DR. Chelaramani et al [5] further stated that AMD affects 6.2 million people globally.…”

Section: Introductionmentioning

confidence: 99%

Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

Muchuchuti

Viriri

2023

J. Imaging

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Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious and lacks repeatability. There have been efforts to automate ocular disease detection, riding on the successes of the application of Deep Convolutional Neural Networks (DCNNs) and vision transformers (ViTs) for Computer-Aided Diagnosis (CAD). These models have performed well, however, there remain challenges owing to the complex nature of retinal lesions. This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of current deep-learning research for the detection and grading of glaucoma, diabetic retinopathy, Age-Related Macular Degeneration and multiple retinal diseases. The work concluded that CAD, through deep learning, will increasingly be vital as an assistive technology. As future work, there is a need to explore the potential impact of using ensemble CNN architectures in multiclass, multilabel tasks. Efforts should also be expended on the improvement of model explainability to win the trust of clinicians and patients.

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Accurate Diagnosis of Diabetic Retinopathy and Glaucoma Using Retinal Fundus Images Based on Hybrid Features and Genetic Algorithm

Cited by 12 publications

References 43 publications

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

An investigation of Glaucoma Detection in RFI Using Bio-Inspired Multivariable FE with ML Models for Early Diagnosis

Artifical intelligence with optimal deep learning enabled automated retinal fundus image classification model

Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

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