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A hybrid regularized extreme learning machine for automated detection of pathological brain

Nayak Deepak Ranjan, Dash Ratnakar, Majhi Banshidhar, Zhang Yudong

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 3

880--892

This paper presents an automated method for detection of pathological brain using magnetic resonance (MR) images. The proposed method suggests to derive features using fast discrete curvelet transform. A combined feature reduction algorithm principal component analysis + linear discriminant analysis (PCA + LDA) is then applied to generate a low-dimensional and discriminant feature vector. Finally, the classification is carried out using a hybrid regularized extreme learning machine (RELM). The proposed hybrid classifier combines RELM and sine cosine algorithm to not only overcome the drawbacks of conventional learning algorithms but also provide good generalization performance with a compact and well-conditioned network. Besides root mean square error, the norm of the output weights and the condition value of the hidden layer output matrix have been separately taken into consideration to optimize the parameters of RELM. Extensive simulations on three benchmark datasets demonstrate that the proposed scheme obtains promising results as compared to state-of-the-art approaches. The effectiveness of the proposed hybrid classifier is compared with its counterparts. Moreover, the impact of noise in the training data is analyzed using the suggested scheme. The proposed approach can aid the radiologists for screening pathological brain at a larger scale.