Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

Ramani, R.; Sugirtharani, S; Lakshmi, Baddireddi Subhadra

doi:10.5120/ijca2017914130

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Therefore, the proportion of fat and other substances can be measured by the electrical resistance of the body [ 14 ]. In the 15-second kick measurement, the subject used the attacking leg as the upper kick and the other leg as the supporting leg in a badminton match [ 15 ]. The number of kicks in 15 seconds is measured and tested with two heads: the subject lies flat on the mat with the arms straight, the shoulder blades must touch the mat, the knees are straight, and the head and hands are raised simultaneously.…”

Section: Data Mining and Research Methodsmentioning

confidence: 99%

Changes in Physical Function of Shuttlecock Players after Short-Term Intensive Training based on Data Mining

Zeng

2022

Computational Intelligence and Neuroscience

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This paper investigates the changes in body composition and physical fitness of badminton players after short-term high-intensity training. Methods. During an 8-week short-term training camp, 12 nonprofessional badminton players were paired to design training sessions, and body composition and fitness were measured before and after training. Results. After 8 weeks of high-intensity training, there was no significant change in body weight of badminton players P > 0.05 , while lean body mass increased significantly and body fat percentage decreased significantly P < 0.05 . The time of kicking, lying on the back, and standing forward was increased P < 0.05 , and the time of running back and forth 6.1 meters 3 times was shorter than that before training P < 0.05 . Conclusion. The increase of lean body mass can promote the energy conversion, oxygen consumption, and exercise capacity of athletes. Short-term training can reduce excess body fat, promote muscle development, and improve physical fitness.

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Section: Data Mining and Research Methodsmentioning

confidence: 99%

Changes in Physical Function of Shuttlecock Players after Short-Term Intensive Training based on Data Mining

Zeng

2022

Computational Intelligence and Neuroscience

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“…From the results, we can notice that the best performance to impulse noise is median filter and the best for Gaussian noise is an adaptive filter, but for retinal image without noise the (7) Gaussian filter is superior, to get more accurate results the performance of five filtering methods was tested using retinal images with different noises level (Tables 4 & 5). performance are compared to each other based on three parameters: mean squared error (mse), peak signal noise ratio (psnr) and structural similarity (ssim), our experiments have shown that the best performance founded for impulse noise is an adaptive median filter and for Gaussian noise is an adaptive filter.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Spatial Filtering Methods in Retinal Fundus Images

Kamran¹

2023

Preprint

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The denoising of the fundus images is an essential pre-processing step in glaucoma diagnosis to ensure sufficient qualityfor the Computer Aided Diagnosing (CAD) system. In this paper, we present an evaluation approach for different denoising filters ofeye fundus images that suffer from two different types of noises (Gaussian noise and Salt & Pepper noise), which had been applied tothe retinal images and then various Spatial filtering techniques like linear (Gaussian, mean), nonlinear filtering (median) and adaptivefiltering have been implemented to three types of images (original images, images with salt and pepper noise and images with Gaussiannoise) and their performance are compared to each other based on evaluation parameters: Mean Squared Error (MSE), Peak SignalNoise Ratio (PSNR) and Structural Similarity (SSIM). The results showed that the adaptive median filter has the best performance insalt & paper noise and the adaptive filter has the best performance for Gaussian noise, but their performance is close to each other. Inconclusion, six spatial filters applied to RIM-ONE fundus image database and found that, the adaptive median filter has the bestperformance compared to other filters to remove these noises and increase the quality of the resulting images, which can be implementedto the CAD system.

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“…There is a usable value, and the meaning that it cannot be divided again means that in the current branch, the feature has only a unique value. The characteristics such as the type and the size of the data volume select an appropriate algorithm to process the data [17]. As we all know, the ID3 algorithm is mainly for discrete variables.…”

Section: Decision Tree Algorithmmentioning

confidence: 99%

Sports Policy and Training Decision Support Method Based on Wireless Sensor Network

2021

Wireless Communications and Mobile Computing

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In recent years, the awareness of sports departments at all levels of society to promote sports through science and has been increasing, and the scientific decision-making and management of sports have been improved to a great extent. With the application of scientific decision-making combined with a real-time sports data monitoring network, the opponent’s advance information can be effectively observed during the game and reasonable decisions can be made to deal with the opponent’s offense. Therefore, high-level athletes appear to be more relaxed and calm in the game. It first requires the application of advanced information collection methods to obtain sports data quickly, in real time and at low cost, and extract information about athletes’ scientific management decision-making from massive data and then make scientific management decisions for sports training. The modern sports method is highly open, and big data mining also profoundly affects the relevant decision-making of sports training. How to design appropriate decision support tools to grasp the key points of the problem in sports information data and make reasonable and correct decisions is a problem that is closely watched by macro decision-makers and coaches at all levels. This article mainly introduces the training decision support method derived from data mining and intends to provide some technical directions for making scientific decisions in sports training. This paper proposes related algorithms of a training decision support method derived from data mining, including training effectiveness prediction model and decision tree algorithm, for the design of the training decision support method derived from data mining. Experimental data shows that the average error between the prediction of the effectiveness of the training method and the actual situation of the training decision support method in this paper is 0.913%, which is helpful for the management or coach to make decisions.

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Automatic Detection of Glaucoma in Retinal Fundus Images through Image Processing and Data Mining Techniques

Cited by 8 publications

References 17 publications

Changes in Physical Function of Shuttlecock Players after Short-Term Intensive Training based on Data Mining

Changes in Physical Function of Shuttlecock Players after Short-Term Intensive Training based on Data Mining

Evaluation of Spatial Filtering Methods in Retinal Fundus Images

Sports Policy and Training Decision Support Method Based on Wireless Sensor Network

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