Computer-Aided System for Automatic Classification of Suspicious Lesions in Breast Ultrasound Images

Karimi, Behnam; Krzyżak, Adam

doi:10.1007/978-3-319-07176-3_12

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…One of the most known ones in pattern recognition is SVM . This classifier separates the training data into two classes from {( a 1 , b 1 ), ( a 2 , b 2 ), …, ( a N , b N )}, where a i ∈ R D is a D dimensional feature space, and b i in {−1, +1} is the class label, with i ∈ [1, N] . SVM constructs the best hyperplane that separates the classes using a kernel function k. The data with a feature vector that belongs to one side are considered as belonging to the first class (denoted as −1), and the other data are considered as belonging to the second class (denoted as +1) .…”

Section: Methodsmentioning

confidence: 99%

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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