A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

Mahmood, Mohammed Arif Iftakher; Aktar, Nasrin; Kader, Md. Fazlul

doi:10.1016/j.heliyon.2023.e19625

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…It is crucial to accurately identify different lesions across different phases despite variations in image quality and eye movement. Also, most of the methods presented in the Literature have not been tested in real patients (4,14,20,22,23,25,26). (28,29).…”

Section: Discussionmentioning

confidence: 99%

“…The Isodata thresholding technique is an iterative method that uses the Euclidean distance to estimate the similarity measure in the clustering of data elements into different classes. This technique has been suggested in several articles for fundus images as an effective method (20)(21)(22)(23). Most of these articles were analyzed on different types of standard databases, and less on real patients with low quality, noise, and artifacts.…”

Section: Isodata-based Segmentationmentioning

confidence: 99%

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A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

Zare,

Ramezanzadeh,

Shoeibi

et al. 2024

Preprint

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This study aims to develop an advanced automatic method to enhance the accuracy of blood vessel segmentation in Fundus Fluorescein angiography FFA images of patients with real Diabetic retinopathy (DR) during both early and late phases. A prospective randomized study of 280 eyes of 87 DR patients at various stages was included in this study. our approach involved using four different image enhancement techniques (Histogram equalization (HE), CLAHE, RMSHE, Proposed method (CLAHE+RMSHE)) and for each enhancement except HE, four thresholding methods including the proposed method (Modified Active Contour(MAC)+ IsoData), C-mean Fuzzy, Isodata, and MAC + otsu thresholding that are commonly used in eye fundus images across different datasets. The effectiveness of segmentation was evaluated using three metrics: Dice (early: 0.84±0.05, late:0.84±0.03), Jaccard (early:0.73±0.06, late:0.74±0.05), and BF score (early:0.98±0.02, late:0.97±0.02). We found that the RMSHE method was more effective in the late phase, while the combined CLAHE+RMSHE method worked better in the early phase. The best segmentation results for both early and late phases were achieved using a blood vessel segmentation approach based on the MAC + IsoData thresholding.

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Section: Discussionmentioning

confidence: 99%

Section: Isodata-based Segmentationmentioning

confidence: 99%

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

Zare,

Ramezanzadeh,

Shoeibi

et al. 2024

Preprint

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show abstract

“…A timely intervention, adjustments to one's lifestyle, and the implementation of individualized healthcare strategies are all made possible through the early detection of diabetes. The prompt initiation of treatment not only improves health outcomes, but it also reduces the financial burden that is associated with the management of advanced phases of diabetes and the complications that are associated with it [8].…”

Section: Figure 1 Normal Retina Vs Diabetic Retinamentioning

confidence: 99%

Customized Mechanism for Diabetic Risk Prediction: A Hybrid CNN–Autoencoder Approach with Emphasis on Retinal Imaging in the Elderly

Drakshayani Desai

2024

jes

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Diabetes Mellitus presents a substantial health obstacle on a global scale, with a particular impact on the elderly demographic. Prompt identification is vital for efficient control and avoidance of complications. This study introduces a new Hybrid Convolutional Neural Network (CNN) and Autoencoder model specifically developed for accurately predicting the risk of diabetes at an early stage. The model is specifically designed to analyze retinal images in older individuals. The introduction of this paper presents a comprehensive analysis of the increasing incidence of diabetes in the elderly population and underscores the significance of early identification. Conventional approaches frequently encounter constraints in terms of precision and specificity, which has led to the investigation of sophisticated machine learning models. The Hybrid CNN–Autoencoder model combines the advantageous characteristics of both architectures, utilizing the CNN proficiency in extracting spatial features and the Autoencoder's capability for unsupervised feature learning. The approach we use consists of training and validating the model using a comprehensive dataset of retinal images from elderly individuals. The model attains a remarkable accuracy of 90.92%, outperforming the typical deep learning and machine learning models frequently employed in predicting diabetic risk. The experimental results demonstrate the superior performance of the Hybrid CNN–Autoencoder model in terms of accuracy, sensitivity, and specificity. Comparative analysis shows that it is highly effective in identifying subtle patterns that indicate early signs of diabetes, surpassing traditional models and other modern deep learning methods. The research findings presented in this study make a valuable contribution to the expanding knowledge base on diabetes detection, specifically within the elderly population. The proven precision of the suggested model highlights its capacity as a dependable and tailored instrument for early forecasting, thus enabling prompt interventions and individualized healthcare strategies for individuals susceptible to diabetes.

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NO transport characteristics in microcirculation and its implications in the assessment of microvascular dysfunction in type 2 diabetes mellitus

He,

Liu,

Long

et al. 2024

Medicine in Novel Technology and Devices

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A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features

Cited by 7 publications

References 41 publications

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

A high-accuracy segmentation Hybrid method for retinal blood vessel detection in Fluorescein Angiography images of real Diabetic Retinopathy patients

Customized Mechanism for Diabetic Risk Prediction: A Hybrid CNN–Autoencoder Approach with Emphasis on Retinal Imaging in the Elderly

NO transport characteristics in microcirculation and its implications in the assessment of microvascular dysfunction in type 2 diabetes mellitus

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