An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images

Dayana, A. Mary; Emmanuel, W. R. Sam

doi:10.1007/s11042-022-12492-0

Cited by 33 publications

(5 citation statements)

References 41 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%

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%

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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“…The diferential evolution (DE) algorithm is used to enhance GSO. An effective, optimized DNN using the Chronological Tunicate Swarm Algorithm (CTSA) is proposed by Dayana et al [16]. Poor quality fundus photos are utilized in the segmentation process after being preprocessed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

Karadayi Ataş

2024

CTBEB

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A major contributor to blindness in people of working age worldwide is diabetic retinopathy (DR), which is mostly caused by vascular damage brought on by high blood sugar in those who have diabetes mellitus. Since early detection greatly reduces the risk of vision loss, it is essential for the effective management of DR. This work presents a novel method that uses artificial intelligence (AI) to improve fundus image analysis for early diagnosis of diabetes mellitus (DR). Understanding how important high-quality images are to precise diagnosis, our approach starts with a sophisticated preprocessing stage. This stage focuses on improving the pictures by removing erroneous dark patches and other distortions, readying the dataset for additional examination. After preprocessing, we utilize an innovative hybrid hyperparameter optimization model that creatively combines the best features of grid and random search techniques. In order to identify early indicators of DR, such as microaneurysms and vessel changes, this model seeks to optimize the parameters of machine learning algorithms. Through a methodical analysis of the preprocessed fundus images, our model more effectively and accurately detects patterns suggestive of early-stage DR. The results of this study not only show that using AI in conjunction with careful preprocessing and hyperparameter optimization is effective in diagnosing DR, but they also open up new avenues for future investigation into the use of AI in early disease detection. In the end, this study makes a substantial contribution to the field of medical imaging and presents a viable treatment option for preventing vision loss in diabetic patients through early diagnostic intervention.

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“…Deep learning models have been successfully employed a wide range of applications in variety of fields (Chen et al., 2023; Dayana et al., 2023; Qiu et al., 2023; Uyar, Solmaz, et al., 2022). Moreover, CNNs are a more sophisticated kind of artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Histological tissue classification with a novel statistical filter‐based convolutional neural network

Ünlükal,

Ülker,

Solmaz

et al. 2024

Anat Histol Embryol

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Deep networks have been of considerable interest in literature and have enabled the solution of recent real‐world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image‐based challenges. The high‐performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter‐based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

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An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images

Cited by 33 publications

References 41 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

A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

Histological tissue classification with a novel statistical filter‐based convolutional neural network

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