Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights

Reddy, V. Purna Chandra; Gurrala, Kiran Kumar

doi:10.1016/j.bspc.2021.103439

Cited by 12 publications

(13 citation statements)

References 35 publications

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“…In [11], the authors proposed a deep learning approach for the joint classification of DR and diabetic macular edema (DME) using a modified grey-wolf optimizer with variable weights. The method utilized a convolutional neural network (CNN) for feature extraction and classification.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 shows the performance comparison of various DR grading classifiers. Here, the proposed DRG-Net resulted in superior performance as compared to MSA-ResNetGB [13], DLCNN-MGWO-VW [11], OHGCNet [12], and E-DenseNet BC-121 [15]. Compared to DLCNN-MGWO-VW [11], the proposed DRG-Net achieves a higher accuracy of 99.93%, surpassing it by 7.7%, while exhibiting a slightly lower precision of 98.49% by 1.2%, a higher recall of 99.95% by 6.74%, and a higher F1-Score of 99.85% by 5.22%.…”

Section: Objective Analysismentioning

confidence: 99%

“…Here, the proposed DRG-Net resulted in superior performance as compared to MSA-ResNetGB [13], DLCNN-MGWO-VW [11], OHGCNet [12], and E-DenseNet BC-121 [15]. Compared to DLCNN-MGWO-VW [11], the proposed DRG-Net achieves a higher accuracy of 99.93%, surpassing it by 7.7%, while exhibiting a slightly lower precision of 98.49% by 1.2%, a higher recall of 99.95% by 6.74%, and a higher F1-Score of 99.85% by 5.22%. Compared to MSA-ResNetGB [13], the proposed DRG-Net demonstrates a significant improvement in accuracy, achieving 99.93% accuracy compared to 94.17% of MSA-ResNetGB [13].…”

Section: Objective Analysismentioning

confidence: 99%

See 2 more Smart Citations

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

2023

IJIES

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Diabetic retinopathy (DR) is a prevalent and potentially blinding eye disease that affects individuals with diabetes. On the other hand, internet of medical things (IoMT) incorporation into DR grading classification offers a promising future for the field of eye health. It optimizes the diagnostic process, increases accessibility to healthcare services, and ultimately improves patient care and outcomes. Therefore, accurate and timely grading of DR severity is crucial for effective disease management. In this article, DR-grading network (DRG-Net) is proposed, which is a comprehensive approach for DR labels classification using the indian diabetic retinopathy image dataset (IDRiD) dataset. To address the imbalanced nature of the dataset, synthetic minority over-sampling technique (SMOTE) is employed for data balancing, ensuring representative samples for each severity level. Then, transfer learning based residual network-50 (ResNet50) architecture is used to extract features from SMOTE outcome, which is a deep learning model renowned for its ability to learn complex image representations. Finally, graph-based K-nearest neighbours (GKNN) classification which utilizes the spatial relationships between samples to make informed decisions, considering the similarity of retinal images in a graph-based representation is introduced for enhanced grading classification of DR. The simulation results show that, the proposed DRG-Net resulted in improved performance as compared to state-of-the-art approaches such as MSA-ResNetGB, DLCNN-MGWO-VW, OHGCNet, and E-DenseNet BC-121 with an accuracy of 99.93%, and F1-score of 99.85%.

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Section: Related Workmentioning

confidence: 99%

Section: Objective Analysismentioning

confidence: 99%

Section: Objective Analysismentioning

confidence: 99%

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DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

2023

IJIES

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“…Continuing on the trail of diabetic retinopathy (DR), ref. [24] showcased a hybrid DL network to detect and grade DR severity. With the utilization of ResNet50, they achieved an accuracy of up to 96% on the IDRiD dataset.…”

Section: Enhanced Classification Performancementioning

confidence: 99%

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases

Abbas,

Albathan,

Altameem

et al. 2023

Diagnostics

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It is difficult for clinicians or less-experienced ophthalmologists to detect early eye-related diseases. By hand, eye disease diagnosis is labor-intensive, prone to mistakes, and challenging because of the variety of ocular diseases such as glaucoma (GA), diabetic retinopathy (DR), cataract (CT), and normal eye-related diseases (NL). An automated ocular disease detection system with computer-aided diagnosis (CAD) tools is required to recognize eye-related diseases. Nowadays, deep learning (DL) algorithms enhance the classification results of retinograph images. To address these issues, we developed an intelligent detection system based on retinal fundus images. To create this system, we used ODIR and RFMiD datasets, which included various retinographics of distinct classes of the fundus, using cutting-edge image classification algorithms like ensemble-based transfer learning. In this paper, we suggest a three-step hybrid ensemble model that combines a classifier, a feature extractor, and a feature selector. The original image features are first extracted using a pre-trained AlexNet model with an enhanced structure. The improved AlexNet (iAlexNet) architecture with attention and dense layers offers enhanced feature extraction, task adaptability, interpretability, and potential accuracy benefits compared to other transfer learning architectures, making it particularly suited for tasks like retinograph classification. The extracted features are then selected using the ReliefF method, and then the most crucial elements are chosen to minimize the feature dimension. Finally, an XgBoost classifier offers classification outcomes based on the desired features. These classifications represent different ocular illnesses. We utilized data augmentation techniques to control class imbalance issues. The deep-ocular model, based mainly on the AlexNet-ReliefF-XgBoost model, achieves an accuracy of 95.13%. The results indicate the proposed ensemble model can assist dermatologists in making early decisions for the diagnosing and screening of eye-related diseases.

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“…In order to detect and grade the severity of DR, Reddy et al [25] introduced a hybrid deep architecture that utilized a modified grey wolf optimizer with variable weights and attention modules to extract disease-specific features. The hybrid system aided in the joint DR-DME classification on the publicly available IDRiD dataset and achieved detection accuracy rates of 96.0%, 93.2%, and 92.23% for DR, DME, and joint DR-DME, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture

et al. 2023

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Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture’s ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.

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Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights

Cited by 12 publications

References 35 publications

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

DRGNet: Diabetic Retinopathy Grading Network Using Data Balancing Integrated Transfer Learning with Graph-based KNN Classification

Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases

Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture

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