Diabetic Retinopathy Detection Using Twin Support Vector Machines

Singla, Manisha; Soni, Samyak; Saini, Prateek; Chaudhary, Abhishek; Shukla, K. K.

doi:10.1007/978-981-15-0339-9_9

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An RF-based classifier was introduced in [5] to detect retinal abnormalities and provide doctors with a useful tool. To avoid the computation time of the SVM, the researcher forecasted the Diabetic Retinopathy by assessing the microaneurysm and region of the fundus images utilizing Twin-SVM (TSVM) instead of a solo SVM [29]. The TSVM is 4 times as quick as the standard SVM.…”

Section: Detection Of Retinal Disease Modelsmentioning

confidence: 99%

Application of Optimized Deep Learning Mechanism for Recognition and Categorization of Retinal Diseases

Alhajim,

Al-Shammari,

Kareem Oleiwi

2024

IJCDS

View full text Add to dashboard Cite

Retinal disorders are one of the common eye problems and its complication affects the eyes. In some cases, the retinal diseases would not cause any symptoms, or they only show mild vision impairments. Finally, it causes no vision or blindness. So, earlier recognition of symptoms could help to avoid blindness. Routine screening is one of the methods for early diagnosis of retinal disease. Another common way to identify retinal disease is to have an expert evaluate and rate eye photographs for the existence and severity of the illness. Unfortunately, in many parts of the world where retinal disease is common, the medical specialists capable of recognizing DR are scarce. Hence, a novel optimized African Buffalo-based deep Convolutional Neural Network (AB-DCNN) deep learning model is introduced in this article, which could detect the retinal disorders in the earlier stage from the fundus retinal image datasets and classify its stages. The proposed mechanism could detect diseases like Central Serous Retinopathy (CSR), Age-related Macular Degeneration (AMD), Diabetic Retinopathy (DR) and Macular hole (MH) and classify its stages as Severe, Moderate, Mild NPDR, PDR, and normal case. Depending upon the clinical importance, the impact of uncertainty on system performance and the relation between explained ability and uncertainty are examined. The uncertainty evidences make the system more reliable for usage in clinical environments. The proposed methodology increases the operational speed and lessens the computation time of the algorithm. It also reduces the losses and enhances classification accuracy.

show abstract

Section: Detection Of Retinal Disease Modelsmentioning

confidence: 99%

Application of Optimized Deep Learning Mechanism for Recognition and Categorization of Retinal Diseases

Alhajim,

Al-Shammari,

Kareem Oleiwi

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…On the one hand, practical ML approaches with supervised methods target pixel-based feature maps as training to identify vessel patterns. Various methodologies have been proposed to detect vessels using k-Nearest Neighbors ( 17 ), Decision Trees ( 18 ), SVM ( 19 ), and Neural Networks ( 20 , 21 ). On the other hand, unsupervised methods employ rule-based algorithms, including filters, gradients, and thresholds for the same classification purpose.…”

Section: Literature Reviewmentioning

confidence: 99%

Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net

Bhatia

Alam

Shuaib

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset.

show abstract

“…Twin support vector machines (TWSVMs) were used for DR detection. The authors used digital fundus images which are fed to the TWSVMs [25]. In [26], they proposed a computer-aided diagnosis (CAD) system for detecting early-stage DR using OCTA images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine

Abdelsalam

Zahran

2021

IEEE Access

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) is a complication of diabetes that affects the eyes. It is caused by blood vessel damage of the light-sensitive tissue at the back of the retina. Neovascularization are emerged and the small blood vessels are blocked. The prevention or delaying vision loss can be obtained by DR early detection. The retinal microvascular network as a biological system has its own multifractal features as generalized dimensions, lacunarity and singularity spectrum. In this study, a novel approach for DR early detection based on the multifractal geometry has been proposed in some details. Analyzing the macular optical coherence tomography angiography (OCTA) images for diagnosing early non-proliferative diabetic retinopathy (NPDR). Using a supervised machine learning method as a Support Vector Machine (SVM) algorithm to automate the diagnosis process and improving the resultant accuracy. The classification technique had achieved 98.5 % accuracy. This approach also can classify easily other diabetic retinopathy stages or other retinal diseases, which affect the vessels or neovascularization distribution.

show abstract

Diabetic Retinopathy Detection Using Twin Support Vector Machines

Cited by 6 publications

References 14 publications

Application of Optimized Deep Learning Mechanism for Recognition and Categorization of Retinal Diseases

Application of Optimized Deep Learning Mechanism for Recognition and Categorization of Retinal Diseases

Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net

A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine

Contact Info

Product

Resources

About