Severity Classification of Non-Proliferative Diabetic Retinopathy Using Convolutional Support Vector Machine

Putra, Ricky Eka; Tjandrasa, Handayani; Suciati, Nanik

doi:10.22266/ijies2020.0831.14

Cited by 16 publications

(8 citation statements)

References 26 publications

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“…The two stages of DR are called non-proliferative diabetic retinopathy (NPDR) and PDR [6,7,8]. However, NPDR is the early stage of DR, and it is further distinguished into three sub-categories.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture

Khan

et al. 2021

IEEE Access

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This work was supported by the BK-21 four program through the National Research Foundation of Korea (NRF) under the Ministry of Education. We would also like to present bundle of thanks to Nvidia Corporation for providing a support by donating us a Telsa K-40 GPU. Zubair khan and Farman Ali contributed equally and co-first authors ABSTRACT Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR's different stages effectively. This paper focuses on classifying the DR's different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer's virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture

Khan

et al. 2021

IEEE Access

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“…Both noise problems and non-uniform illumination are ineffectively tackled by the developed model that degrades the classification performance. Putra [17] used morphology contrast enhancement technique, homomorphic method and contrast limited adaptive histogram equalization technique for image resizing and cropping. The pre-processed image was given as the input to deep learning techniques such as ResNet101, GoogLeNet, ResNet50 and ResNet18 for feature extraction.…”

Section: Literature Surveymentioning

confidence: 99%

Microaneurysms Detection Using Grey Wolf Optimizer and Modified K-Nearest Neighbor for Early Diagnosis of Diabetic Retinopathy

2022

IJIES

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In recent decades, Diabetic Retinopathy (DR) is a progressive eye disease that causes severe eye injuries if it is not detected and treated on time. Accurate microaneurysms detection is a vital step for early detection of DR, because it is the primary sign of disease. In this paper, a six-phase model is introduced for detecting microaneurysms from the fundus retinal images for early diagnosis of DR. Initially, lower light retinal image enhancement, image normalization, gradient weighting and shade correction are applied for improving the visibility level of fundus retinal images, which are acquired from the e-ophtha, and DiaRetDB1 datasets. Further, the hessian-based filter, and Otsu thresholding with the morphological operator are employed to eliminate blood vessel regions from the microaneurysms regions. Next, a grey wolf optimizer is used for predicting the correctness of the segmented microaneurysms regions. After segmentation, feature extraction: shape and Gray Level Co-occurrence Matrix (GLCM) features and classification: Modified K Nearest Neighbor (MKNN) are used to extract features from microaneurysms regions and to classify microaneurysms and non-microaneurysms regions. The simulation result showed that the proposed model achieved effective performance in microaneurysms detection compared to the existing models such as H-maximamultilevel thresholding-multilayer perceptron and statistical geometrical features. The proposed model achieved 99.10% and 99.90% of accuracy on e-ophtha and DiaRetDB1 datasets, which are effective related to the existing models in microaneurysms detection.

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“…Putra [19] used contrast limited adaptive histogram equalization, homomorphic and morphology contrast enhancement techniques for improving the quality of collected images. The preprocessed images were fed to feature extraction layer of ResNet101, ResNet50, GoogLeNet, and ResNet18.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Diabetic Retinopathy Using K-means Clustering Algorithm and Ensemble Classification Approach

2021

IJIES

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Severity Classification of Non-Proliferative Diabetic Retinopathy Using Convolutional Support Vector Machine

Cited by 16 publications

References 26 publications

Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture

Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture

Microaneurysms Detection Using Grey Wolf Optimizer and Modified K-Nearest Neighbor for Early Diagnosis of Diabetic Retinopathy

Early Detection of Diabetic Retinopathy Using K-means Clustering Algorithm and Ensemble Classification Approach

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