Detection and Classification of Exudates Using K-Means Clustering in Color Retinal Images

Rajput, G. G.; Patil, Preethi N.

doi:10.1109/icsip.2014.25

Cited by 25 publications

(11 citation statements)

References 6 publications

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“…Clustering is an unsupervised learning process in which similar objects are grouped into a cluster, without having any predefined classes as compared to classification [34]. There are abundant clustering techniques for diabetes prediction in the literature, such as [99,100,[103][104][105][106][107][112][113][114][115][116][117]. In [102], clustering is used to identify the characteristics that could determine the stage of diabetes and the presence of diabetes in a number of women based on the diabetic and normal classified data.…”

Section: Clustering-based Techniquesmentioning

confidence: 99%

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

et al. 2021

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Diabetes is one of the most rapidly growing chronic diseases, which has affected millions of people around the globe. Its diagnosis, prediction, proper cure, and management are crucial. Data mining based forecasting techniques for data analysis of diabetes can help in the early detection and prediction of the disease and the related critical events such as hypo/hyperglycemia. Numerous techniques have been developed in this domain for diabetes detection, prediction, and classification. In this paper, we present a comprehensive review of the state-of-the-art in the area of diabetes diagnosis and prediction using data mining. The aim of this paper is twofold; firstly, we explore and investigate the data mining based diagnosis and prediction solutions in the field of glycemic control for diabetes. Secondly, in the light of this investigation, we provide a comprehensive classification and comparison of the techniques that have been frequently used for diagnosis and prediction of diabetes based on important key metrics. Moreover, we highlight the challenges and future research directions in this area that can be considered in order to develop optimized solutions for diabetes detection and prediction.

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Section: Clustering-based Techniquesmentioning

confidence: 99%

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

et al. 2021

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“…e researchers of [20] conducted an analytical technique for image junk organization using an artificial neural network in their work. It is a useful technique in image organization for locating and resolving feature extraction issues.…”

Section: Related Workmentioning

confidence: 99%

Application of Improved Boosting Algorithm for Art Image Classification

2021

Scientific Programming

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In the field of computer science, data mining is a hot topic. It is a mathematical method for identifying patterns in enormous amounts of data. Image mining is an important data mining technique involving a variety of fields. In image mining, art image organization is an interesting research field worthy of attention. The classification of art images into several predetermined sets is referred to as art image categorization. Image preprocessing, feature extraction, object identification, object categorization, object segmentation, object classification, and a variety of other approaches are all part of it. The purpose of this paper is to suggest an improved boosting algorithm that employs a specific method of traditional and simple, yet weak classifiers to create a complex, accurate, and strong classifier image as well as a realistic image. This paper investigated the characteristics of cartoon images, realistic images, painting images, and photo images, created color variance histogram features, and used them for classification. To execute classification experiments, this paper uses an image database of 10471 images, which are randomly distributed into two portions that are used as training data and test data, respectively. The training dataset contains 6971 images, while the test dataset contains 3478 images. The investigational results show that the planned algorithm has a classification accuracy of approximately 97%. The method proposed in this paper can be used as the basis of automatic large-scale image classification and has strong practicability.

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“…Rajput et al, [6] have eliminated the noise and later blood vessels network to facilitate the detection of optic dye and later classified based on energy and threshold. OD has been eliminated with an accuracy of 100% and increased efficiency.…”

Section: Related Workmentioning

confidence: 99%

Object Detection using K-Means Clustering – A Research

2019

IJRTE

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Clustering is an unsupervised machine learning technique and the task is to group a set of objects in such a way that objects in the same group are more similar to each other than those in other groups. There are different clustering techniques, each with its own advantages and disadvantages. The K-means clustering algorithm is our main focus in this paper. K-means is mostly used when there is a large number of unlabeled data. The difficulties in the path of K-means clustering are a) Different initial points can lead to different final clusters. b) It does not work with clusters of different sizes and densities. c) With the global cluster, it does not work well and is difficult to determine K Value. Our main aim is to try and modify the K-means clustering algorithm to get rid of the above-mentioned drawbacks.

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Detection and Classification of Exudates Using K-Means Clustering in Color Retinal Images

Cited by 25 publications

References 6 publications

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

Detection and Prediction of Diabetes Using Data Mining: A Comprehensive Review

Application of Improved Boosting Algorithm for Art Image Classification

Object Detection using K-Means Clustering – A Research

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