In recent years,the development in the field of computer-aided diagnosis(CAD)has increased rapidly.Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic ...In recent years,the development in the field of computer-aided diagnosis(CAD)has increased rapidly.Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images.The existing algorithms have drawbacks with respect to their accuracy,efficiency,and limited learning processes.To address these issues,we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast,2D-discrete wavelet transformation(2D-DWT)to extract the features,probabilistic principal component analysis(PPCA)and linear discriminant analysis(LDA)to normalize and reduce the features,and a feed-forward neural network(FNN)and modified particle swarm optimization(MPSO)with ant colony optimization(ACO)to improve the accuracy,stability,and overcome fitting issues in the classification of brain magnetic resonance images.The proposed method achieves better results than other existing algorithms.展开更多
文摘In recent years,the development in the field of computer-aided diagnosis(CAD)has increased rapidly.Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images.The existing algorithms have drawbacks with respect to their accuracy,efficiency,and limited learning processes.To address these issues,we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast,2D-discrete wavelet transformation(2D-DWT)to extract the features,probabilistic principal component analysis(PPCA)and linear discriminant analysis(LDA)to normalize and reduce the features,and a feed-forward neural network(FNN)and modified particle swarm optimization(MPSO)with ant colony optimization(ACO)to improve the accuracy,stability,and overcome fitting issues in the classification of brain magnetic resonance images.The proposed method achieves better results than other existing algorithms.
