Classification of brain MRI using SVM and KNN classifier

Wasule, Vijay; Sonar, Poonam

doi:10.1109/ssps.2017.8071594

Cited by 60 publications

(33 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A linear SVM was applied in this study, and previous studies [52,53] proved that the linear SVM works better on small sample datasets. e value of the penalty coefficient "C" was set to 1.0 because many experimental tests have shown that a value of 1.0 can obtain a satisfactory discrimination performance.…”

Section: Parameter Settingmentioning

confidence: 99%

“…e SVM is presently one of the best-known classification techniques and has computational advantages over other classification methods, and many previous studies [52][53][54][55][56] have proven that the linear SVM performs well in small sample datasets. To allow the classifier to generalize unseen data well and to avoid overfitting problems, we introduced the SVM soft margin classifier.…”

Section: Two-sample T-testsmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. is study proposed an ML framework based on coarse-to-fine feature selection. e proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. e method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. e experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. e identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

show abstract

Section: Parameter Settingmentioning

confidence: 99%

Section: Two-sample T-testsmentioning

confidence: 99%

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In this classifier, the testing feature vector is classified by considering k nearest neighbour vector [8]. The distance between training and testing data will be calculated by using Euclidean distance and on the basis of this, value input image will be categorized into category1, category2, category3 or category4.…”

Section: Classificationmentioning

confidence: 99%

Automization of Agriculture Products Defect Detection and Grading using Image Processing System

Patne¹,

Tjprc²

2018

IJCSEITR

View full text Add to dashboard Cite

Grading of fruit is important phase after harvesting and before marketing. The automatic fruit grading system extracts its defective region and grading, according to its level of defection. The classification of Fruit is done into four categories by considering smaller changes in defective parts, so it increases output efficiency and user acceptance level of different fruit categories.In this proposed system of image processing algorithm, rgb2gray method and median filter are used to preprocessed the input image and convert it into gray scaled image; after that input image is segmented using modern

show abstract

“…Image of a digit of the year is classified into 1,2,3,4,5,6,7,8,9, and 0 classes. Within this present study, the authors expect that the result is worthwhile to the direct contribution to the results of the study which are directly related to the preservation of Majapahit Kingdom ancient relic in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

“…SVM is a classifier which utilizes the farthest margin distance from hyperplane [6] [7]. SVM is also commonly known as a method which is able to process data with high dimension without reducing the dimension of particular data [8] [9]. SVM frequently provides an identical model and solution.…”

Section: Introductionmentioning

confidence: 99%

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018

View full text Add to dashboard Cite

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

show abstract

Classification of brain MRI using SVM and KNN classifier

Cited by 60 publications

References 6 publications

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Automization of Agriculture Products Defect Detection and Grading using Image Processing System

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

Contact Info

Product

Resources

About