Diagnosis of Diabetic Retinopathy by Exudates Detection Using Clustering Techniques

Santhi, D.; Manimegalai, D.; Karkuzhali, S.

doi:10.4015/s101623721450077x

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…It contains a participation grid m of size 7 x N. The lattice component m_ij addresses participation of the jth information Y_j to class I. The accompanying advances are performed to identify exudates in macula area [17][18][19][20][21][22][23][24]:…”

Section: Identi Cation Of Exudates In Macula Regionmentioning

confidence: 99%

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

Karkuzhali¹,

Manimegalai

2022

Preprint

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Diabetic Macula Edema (DME) is brought about by collection of extracellular liquid from hyper penetrable vessels inside the macula. DME is one of the main sources of visual impairment among diabetic patients. The patients who are influenced by diabetes and Diabetic Retinopathy (DR) are well on the way to be influenced by DME. The presence of Exudates in macula area is the significant sign of essence of DME. The Optic Disc (OD) and Blood Vessels (BVs) are the retinal milestones divided to find macula and fovea locale. Area of macula focus is vital for portion the anomalies in the macula locale. The irregularity and its seriousness of the sickness in macula district can be distinguished solely after finding the macula place. Pictures from High Resolution Fundus (HRF) database are utilized for testing and examination. The Atanassov’s Intuitionistic fuzzy Sets (A-FIS) and morphological tasks are utilized to portion OD and BVs. With these retinal landmarks, macula is recognized by raphe assessment. The Exudates (EXs) present in macula district are portioned proficiently by inclination based versatile thresholding. Deep Convolution neural network used for classification of DME and non-DME affected images. The division of OD and EXs has accomplished SE of 95.21%, 98.2%, SPE of 82.36%, 97.4% and ACC of 99.21%, 99.23% separately.

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Section: Identi Cation Of Exudates In Macula Regionmentioning

confidence: 99%

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

Karkuzhali¹,

Manimegalai

2022

Preprint

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“…The element is capable of holding a membership value between 0 and 1. Fuzzy inference system has attracted many researchers for classification and prediction of diseases as it is capable of handling uncertain data [15]…”

Section: Introductionmentioning

confidence: 99%

Comparative study on dimensionality reduction for disease diagnosis using fuzzy classifier

Sujatha¹,

Ephzibah²,

Dharinya³

et al. 2018

IJET

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Machine learning is the worldwide recent research technique for various systems as they are intelligent enough to find the solution for classification and prediction problems. The proposed work is about a hybrid genetic fuzzy algorithm that performs an optimal search as well as classification upon uncertain data. The data which is uncertain is suitable for fuzzy classifiers to predict the disease. The hybrid genetic fuzzy system applied on the attributes selects relevant attributes. The selected attributes are fed into the fuzzy classifier. The fuzzy rules are again generated using genetic algorithms. This algorithm is applied on three of the important and bench marking data sets taken from the UCI machine learning repository. The heart disease, Wisconsin breast cancer and Pima Indian diabetes datasets produce classification accuracy as 89.65%, 99.5% and 88.93% respectively. In this article there is a comparative study on few of the feature selection and feature reduction techniques.

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Utilising Deep Learning Techniques, Detect and Categorise Diabetic Retinopathy

Sudhakar Reddy,

Narayanan

2024

Lecture Notes in Networks and Systems

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Diagnosis of Diabetic Retinopathy by Exudates Detection Using Clustering Techniques

Cited by 5 publications

References 16 publications

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

WITHDRAWN: Identification and Prediction of Diabetic Macula Edema in Two-dimensional Fundus Images Using Raphe Assessment and Deep Convolution Neural Networks

Comparative study on dimensionality reduction for disease diagnosis using fuzzy classifier

Utilising Deep Learning Techniques, Detect and Categorise Diabetic Retinopathy

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