Automated Glaucoma Detection from Fundus Images of Eye Using Statistical Feature Extraction Methods and Support Vector Machine Classification

2017 ◽  
pp. 511-521 ◽  
Author(s):  
Abhishek Dey ◽  
Kashi Nath Dey
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Support Vector ◽  
Fundus Images ◽  
Statistical Feature ◽  
Support Vector Machine Classification ◽  
Glaucoma Detection

scholarly journals The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

2016 ◽  
Vol 6 (6) ◽  
pp. 2797
Author(s):  
Anindita Septiarini ◽  
Hamdani Hamdani ◽  
Dyna Marisa Khairina
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector ◽  
Fundus Images ◽  
Contour Extraction ◽  
Fundus Image ◽  
Feature Extraction Method ◽  
Glaucoma Detection ◽  
Vector Machines ◽  
The World

<p>Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%.</p>

scholarly journals Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura

2018 ◽  
Author(s):  
S Kazemi ◽  
P Katibeh
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Methods ◽  
Dominant Frequency ◽  
Support Vector ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Non Parametric

Background: Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and diagnostic accuracy. Previous work on the subject was controversial, hence a comparison of these methods seems necessary.Objectives: The aim of this research is to compare two parametric and non-parametric feature extraction methods and also two classification methods in order to obtain optimal combinations of diagnostic accuracy.Materials and Methods: Having recorded background EEG from 24 pediatric migraineurs and 19 control subjects, data was processed by Welch’s and Yule-Walker’s methods. Features were selected using genetic algorithm, and then given to a support vector machine and the linear discriminant analysis for the classification. Accuracy was calculated for all combinations having the dominant frequency and the correlated absolute power of each EEG wave band (theta, alpha, and beta) and for all wave bands combined.Results: The highest migraine detection accuracy of 93% was obtained utilizing Welch’s method for EEG feature extraction alongside support vector machine for a classifier. Besides, Yule-Walker autoregressive method showed better performance than Welch’s, when only power bands (and not the dominant frequency) were used as classification input.Conclusion: The superiority of Welch’s method over Yule-Walker’s and the support vector machine over linear discriminant analysis can be great help for further researches on computer aided EEG-based diagnosis of migraine

scholarly journals Disease Detection of Dragon Fruit Stem Based on The Combined Features of Color and Texture

2021 ◽  
Vol 5 (2) ◽  
pp. 161-175
Author(s):  
Lutfi Hakim ◽  
Sepyan Purnama Kristanto ◽  
Dianni Yusuf ◽  
Mohammad Nur Shodiq ◽  
Wahyu Ade Setiawan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Texture Feature ◽  
Extraction Methods ◽  
Support Vector ◽  
Identification System ◽  
Accuracy Score ◽  
K Nearest Neighbors ◽  
Dragon Fruit ◽  
Combined Features

Dragon fruit is one of the favorite commodities in Banyuwangi Regency's agriculture. In 2019, this commodity had the fourth largest harvest area among other fruit commodities in Banyuwangi until it was exported to China. However, disease attacks often appeared in several dragon fruit plantations in Banyuwangi, and the identification system was still conventional. Many farmers did not know the types of disease and how to handle it, causing the quality and quantity of their crops to decline. Therefore, this study implemented two feature extraction methods. Both methods include color feature extraction using the color moments method and texture feature extraction using gray level co-occurrence matrices (GLCM). The methods used to develop a system that recognized or detected the three types of dragon fruit stem based on digital image processing using Support Vector Machine and k-Nearest Neighbors methods as comparison methods. The results obtained from this study indicated that the combination of the two proposed feature extraction methods could distinguish between stem rot, smallpox, and insect stings with an optimal accuracy score of 87.5% obtained by using Support Vector Machine as a classification method.

The Contour Extraction of Cup in Fundus Images for Glaucoma Detection

2016 ◽  
Vol 6 (6) ◽  
pp. 2797
Author(s):  
Anindita Septiarini ◽  
Hamdani Hamdani ◽  
Dyna Marisa Khairina
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector ◽  
Fundus Images ◽  
Contour Extraction ◽  
Fundus Image ◽  
Feature Extraction Method ◽  
Glaucoma Detection ◽  
Vector Machines ◽  
The World

<p>Glaucoma is the second leading cause of blindness in the world; therefore the detection of glaucoma is required. The detection of glaucoma is used to distinguish whether a patient's eye is normal or glaucoma. An expert observed the structure of the retina using fundus image to detect glaucoma. In this research, we propose feature extraction method based on cup area contour using fundus images to detect glaucoma. Our proposed method has been evaluated on 44 fundus images consisting of 23 normal and 21 glaucoma. The data is divided into two parts: firstly, used to the learning phase and secondly, used to the testing phase. In order to identify the fundus images including the class of normal or glaucoma, we applied Support Vector Machines (SVM) method. The performance of our method achieves the accuracy of 94.44%.</p>

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