An automated exudate detection scheme supporting diabetic retinopathy screening using spatial-spectral-statistical feature maps

Remya, K R; Giriprasad, M. N.

doi:10.1007/s11042-022-12354-9

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…It is challenging to treat DR when it is severe. As a result, it's critical to identify DR at an early stage to develop and implement effective management methods [10][11][12][13]. As a result, numerous methods are created to identify the DR lesions and analyze their level.…”

Section: Introductionmentioning

confidence: 99%

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Bapatla¹,

Harikiran²

2023

Computer Systems Science and Engineering

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Diabetes problems can lead to an eye disease called Diabetic Retinopathy (DR), which permanently damages the blood vessels in the retina. If not treated early, DR becomes a significant reason for blindness. To identify the DR and determine the stages, medical tests are very labor-intensive, expensive, and timeconsuming. To address the issue, a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper. Our proposal is based on lesion segmentation of the fundus images based on the LuNet network. Then a Refined Attention Pyramid Network (RAPNet) is used for extracting global and local features. To increase the performance of the classifier, the unique features are selected from the extracted feature set using Aquila Optimizer (AO) algorithm. Finally, the LightGBM model is applied to classify the input image based on the severity. Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets (MESSIDOR, APTOS, and IDRiD) using several performance metrics such as accuracy, precision, recall, and f1-score. The proposed classifier achieves 99.29%, 99.35%, and 99.31% accuracy for these three datasets respectively. The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.

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Section: Introductionmentioning

confidence: 99%

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Bapatla¹,

Harikiran²

2023

Computer Systems Science and Engineering

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Retinal image analysis for detection of diabetic retinopathy- a simplified approach

B.M.

et al. 2024

Multimed Tools Appl

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DenseExudatesNet: a novel approach for hard exudates detection in retinal images using deep learning

Pratheeba,

Rufus

2024

Int. J. Mach. Learn. & Cyber.

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An automated exudate detection scheme supporting diabetic retinopathy screening using spatial-spectral-statistical feature maps

Cited by 3 publications

References 20 publications

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

LuNet-LightGBM: An Effective Hybrid Approach for Lesion Segmentation and DR Grading

Retinal image analysis for detection of diabetic retinopathy- a simplified approach

DenseExudatesNet: a novel approach for hard exudates detection in retinal images using deep learning

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