Diabetic retinopathy detection by optimized deep learning model

Rachapudi, Venubabu; Rao, K. Subba; Rao, Tianmin; Dileep, P.; Roy, T.L. Deepika

doi:10.1007/s11042-023-14606-8

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…The comparison of proposed LBACS-LSTM with the existing approaches such as three-dimensional semantic model ( Jebaseeli et al, 2019 ), KNN ( Rachapudi et al, 2023 ), Computer-aided method ( Shaukat et al, 2022 ), CTSA-SAE ( Dayana and Emmanuel, 2022a ; Dayana and Emmanuel, 2022b ), DS-KL ( Mondal et al, 2023 ), ESOA optimized hybrid RCNN-BiGRU ( Alajlan and Razaque, 2023 ) and Adaptive CNN ( Math and Fatima, 2021 ) are described in this section. Table 10 presents the comparative analysis for IDRiD dataset and Table 11 depicts the comparative analysis of DIARETDB 1 dataset.…”

Section: Results and Analysismentioning

confidence: 99%

“…The results from Table 10 and Table 11 show that the proposed LBACS-LSTM achieves better performance in overall metrics when compared with the existing three-dimensional semantic models ( Jebaseeli et al, 2019 ), KNN ( Rachapudi et al, 2023 ), Computer-aided method ( Shaukat et al, 2022 ), CTSA-SAE ( Dayana and Emmanuel, 2022b ), DS-KL ( Mondal et al, 2023 ) and ESOA optimized hybrid RCNN-BiGRU ( Alajlan and Razaque, 2023 ) and Adaptive CNN ( Math and Fatima, 2021 ). The accuracy of the proposed method for the IDRiD dataset is 99.43% and 97.39% for the DIARETDB 1 dataset.…”

Section: Results and Analysismentioning

confidence: 99%

“… Rachapudi et al (2023) developed an optimized classification method using the Deep Neural Network and Butterfly Optimization Algorithm (DNN-BOA). After the data acquisition and pre-processing, the blood vessels were removed with the help of Gray Level thresholding, and segmentation was performed with the help of Modified Expectation Maximization (MEM).…”

Section: Related Workmentioning

confidence: 99%

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Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Bhansali,

Patra,

Abouhawwash

et al. 2023

Front. Bioeng. Biotechnol.

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Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Bhansali,

Patra,

Abouhawwash

et al. 2023

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

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“…It is noteworthy that in the detection and classification of many DR diseases today, swarm-based optimization algorithms are used either to improve the parameters of the proposed AI-based algorithms to overcome the hyperparameter problem or to select the features that maximize the classifier performance [17][18][19][20][21][22]. In [17], a model was proposed for the DR classification problem in which CNN parameters were optimized with a hybrid genetic and ant colony optimization (HGACO) algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

Overcoming Nonlinear Dynamics in Diabetic Retinopathy Classification: A Robust AI-Based Model with Chaotic Swarm Intelligence Optimization and Recurrent Long Short-Term Memory

2023

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Diabetic retinopathy (DR), which is seen in approximately one-third of diabetes patients worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated in time. It is vital to limit the progression of DR disease in order to prevent the loss of vision in diabetic patients. It is therefore essential that DR disease is diagnosed at an early phase. Thanks to retinal screening at least twice a year, DR disease can be diagnosed in its early phases. However, due to the variations and complexity of DR, it is really difficult to determine the phase of DR disease in current clinical diagnoses. This paper presents a robust artificial intelligence (AI)-based model that can overcome nonlinear dynamics with low computational complexity and high classification accuracy using fundus images to determine the phase of DR disease. The proposed model consists of four stages, excluding the preprocessing stage. In the preprocessing stage, fractal analysis is performed to reveal the presence of chaos in the dataset consisting of 12,500 color fundus images. In the first stage, two-dimensional stationary wavelet transform (2D-SWT) is applied to the dataset consisting of color fundus images in order to prevent information loss in the images and to reveal their characteristic features. In the second stage, 96 features are extracted by applying statistical- and entropy-based feature functions to approximate, horizontal, vertical, and diagonal matrices of 2D-SWT. In the third stage, the features that keep the classifier performance high are selected by a chaotic-based wrapper approach consisting of the k-nearest neighbor (kNN) and chaotic particle swarm optimization algorithms (CPSO) to cope with both chaoticity and computational complexity in the fundus images. At the last stage, an AI-based classification model is created with the recurrent neural network-long short-term memory (RNN-LSTM) architecture by selecting the lowest number of feature sets that can keep the classification performance high. The performance of the DR disease classification model was tested on 2500 color fundus image data, which included five classes: no DR, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The robustness of the DR disease classification model was confirmed by the 10-fold cross-validation. In addition, the classification performance of the proposed model is compared with the support vector machine (SVM), which is one of the machine learning techniques. The results obtained show that the proposed model can overcome nonlinear dynamics in color fundus images with low computational complexity and is very effective and successful in precisely diagnosing all phases of DR disease.

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A Novel Automated System for Early Diabetic Retinopathy Detection and Severity Classification

Ainapur,

Patil

2024

Journal of Chemometrics

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Diabetes is a common and serious global disease that damages blood vessels in the eye, leading to vision loss. Early and accurate diagnosis of this issue is crucial to reduce the risk of visual impairment. The typical deep learning (DL) methods for diabetic retinopathy (DR) grading are often time‐consuming, resulting in unsatisfactory detection performance due to inadequate representation of lesion features. To overcome these challenges, this research proposes a new automated mechanism for detecting and classifying DR, aiming to identify DR severities and different stages. To figure out and capture feature characteristics from DR samples, a conjugated attention mechanism and vision transformer are utilized within a collective net model, which automatically generates feature maps for diagnosing DR. These extracted feature maps are then fused through the feature fusion function in a fused attention net model, calculating attention weights to produce the most powerful feature map. Finally, the DR cases are identified and discriminated using the kernel extreme learning machine (KELM) model. For evaluating DR severity, our work utilizes four different benchmark datasets: APTOS 2019, MESSIDOR‐2 dataset, DiaRetDB1 V2.1, and DIARETDB0 datasets. To illuminate data noise and unwanted variations, two preprocessing steps are carried out, which include contrast enhancement and illumination correction. The experimental results evaluated using well‐known indicators demonstrate that the suggested method achieves a higher accuracy of 99.63% compared to other baseline methods. This research contributes to the development of powerful DR screening techniques that are less time‐consuming and capable of automatically identifying DR severity levels at a premature level.

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Diabetic retinopathy detection by optimized deep learning model

Cited by 9 publications

References 40 publications

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Level-set based adaptive-active contour segmentation technique with long short-term memory for diabetic retinopathy classification

Overcoming Nonlinear Dynamics in Diabetic Retinopathy Classification: A Robust AI-Based Model with Chaotic Swarm Intelligence Optimization and Recurrent Long Short-Term Memory

A Novel Automated System for Early Diabetic Retinopathy Detection and Severity Classification

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