An MR brain images classification technique via the Gaussian radial basis kernel and SVM

Neffati, Syrine; Taouali, Okba

doi:10.1109/sta.2017.8314948

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…KPCA is a nonlinear form of PCA . It is an acknowledged method for feature extraction and dimensionality reduction.…”

Section: Methodsmentioning

confidence: 99%

“…In our work a multikernel SVM (MKSVM) and a regular SVM with RBF kernel were used to classify MRI data. First, a multiclass classification is performed to determine the level of AD using multiclass SVM . Then, the AD was differentiated from the NCA using a binary SVM classification.…”

Section: Methodsmentioning

confidence: 99%

“…KPCA is a nonlinear form of PCA. 2,18,19 It is an acknowledged method for feature extraction and dimensionality reduction. When the PCA method permits only linear dimensionality reduction, KPCA makes a nonlinear one possible by mapping the data with a nonlinear transformation in a new space.…”

Section: Kpcamentioning

confidence: 99%

“…First, a multiclass classification is performed to determine the level of AD using multiclass SVM. 2,7,18 Then, the AD was differentiated from the NCA using a binary SVM classification. The performances of DKPCA with multi kernel SVM and DKPCA with regular SVM are presented in the following section.…”

Section: Multiple Kernel Learningmentioning

confidence: 99%

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An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Neffati

Abdellafou

Jaffel

et al. 2018

Int J Imaging Syst Tech

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Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.

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“…KPCA is a nonlinear form of PCA . It is an acknowledged method for feature extraction and dimensionality reduction.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Kpcamentioning

confidence: 99%

Section: Multiple Kernel Learningmentioning

confidence: 99%

See 2 more Smart Citations

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Neffati

Abdellafou

Jaffel

et al. 2018

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…When the data allows for a linear boundary, the linear kernel showcases outstanding performance, achieving the best accuracy, robust precision and sensitivity, and finally a good F1 score. On the other hand, the RBF kernel [27] is well-suited for complex, nonlinear data but achieves slightly lower accuracy when compared to the linear kernel. Nonetheless, it still delivers respectable precision, recall, and F1-score.…”

Section: Svm -Kernel Trickmentioning

confidence: 99%

SVM-Based Classifier For Early Detection Of Alzheimer's Disease

Niranjan Kumar

2024

EATP

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Alzheimer's disease (AD) is a disorder affecting the brain and its functions that results in permanent damage to the brain. This multifaceted disease slowly destroys brain cells, reducing a person's ability to think, remember, and carry out even the most basic duties. Ultimately, this cognitive decline leads to dementia. A significant portion of the global population faces metabolic challenges like Alzheimer's disease and diabetes. Recent research has explored various machine learning approaches aimed at early disease detection. Early AD diagnosis is very important for a speedy recovery and minimizing damage to brain cells. In this proposed work, a machine learning model is developed using Support Vector Machine to detect individuals with dementia (AD) or without dementia (NC). Our model is trained using 2D Magnetic Resonance Imaging (MRI) brain scan images. We computed common performance measures like the F1-score and accuracy to assess the model. The 15-fold cross-validation technique was applied to crossvalidate these results. Notably, our results demonstrate that Support Vector Machine (SVM) attained a remarkable accuracy of 99.06% in the detection of AD. Accurate Alzheimer's disease detection through machine learning algorithms can greatly reduce the annual mortality rates related to AD.

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