Localization of optic disc using Fuzzy C Means clustering

Padmanaban, K.; Kannan, R. Jagadeesh

doi:10.1109/icctet.2013.6675941

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…Optimal clustering of data items becomes difficult through traditional approaches when compared with neural network clustering. The machine can easily classify the class of iris flower when implementing the existing dataset of iris flowers for clustering [3]. Nowadays, pattern recognition and machine learning have been used in many fields.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Over Support Vector Machine for Better Accuracy in Identifying the Problem Based on the Iris Flower

Poojitha

Kanagasabai

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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The iris dataset will be classified using the support vector machine and decision tree algorithms. flower dataset identifies the pattern and classifies it. The dataset has 150 rows and 5 attributes, which contains 50 samples from each species. There are three species in this dataset. Iris flower classification can be performed using support vector machines and decision tree algorithms. SVM stands for Support Vector Machine, and is a supervised machine learning technique that can be used for classification and regression. The Decision Tree algorithm is a simple approach mainly used for classification and prediction. The sample size has been determined to be 20 for both the groups using G Power 80%. The Support Vector Machine algorithm provides a mean accuracy of 98.09% when compared to the Decision Tree algorithm, with a mean accuracy of 95.55%. A statistically insignificant difference was observed between the Decision Tree and the Support Vector Machine, p = 0.92 (> 0.05) based on 2-tailed analysis. In the classification of Iris flowers, the Support Vector Machine outperformed the Decision Tree Algorithm.

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Section: Introductionmentioning

confidence: 99%

Decision Tree Over Support Vector Machine for Better Accuracy in Identifying the Problem Based on the Iris Flower

Poojitha

Kanagasabai

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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“…Li and Chutatape [ 5 ] presented an algorithm that uses the clustering of image according to the bright pixel and the candidate regions are passed through principal component analysis (PCA) in order to locate the center of optic disc region. Padmanaban and Kannan [ 6 ] suggested the use of Fuzzy C-Means (FCM) clustering. Foracchia et al [ 7 ] worked on tracing the vessels, matching their path with directional pattern in OD in originate image.…”

Section: Introductionmentioning

confidence: 99%

Localizing Optic Disc in Retinal Image Automatically with Entropy Based Algorithm

Muhammed

2018

International Journal of Biomedical Imaging

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Examining retinal image continuously plays an important role in determining human eye health; with any variation present in this image, it may be resulting from some disease. Therefore, there is a need for computer-aided scanning for retinal image to perform this task automatically and accurately. The fundamental step in this task is identification of the retina elements; optical disk localization is the most important one in this identification. Different optical disc localization algorithms have been suggested, such as an algorithm that would be proposed in this paper. The assumption is based on the fact that optical disc area has rich information, so its entropy value is more significant in this area. The suggested algorithm has recursive steps for testing the entropy of different patches in image; sliding window technique is used to get these patches in a specific way. The results of practical work were obtained using different common data set, which achieved good accuracy in trivial computation time. Finally, this paper consists of four sections: a section for introduction containing the related works, a section for methodology and material, a section for practical work with results, and a section for conclusion.

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