Combining extreme learning machine with modified sine cosine algorithm for detection of pathological brain. (May 2018)

• Record Type: Journal Article
• Authors: Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar; Wang, Shuihua
• Is Part Of: Computers & electrical engineering. Volume 68(2018)
• Page Start: 366
• Page End: 380
• Publication Date: 2018-05
• Subjects: Pathological brain detection -- Magnetic resonance imaging -- Fast discrete curvelet transform -- Extreme learning machine -- Modified sine cosine algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.04.009
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Development of automated diagnosis systems has taken a major place in current research practice to assist medical experts in decision-making. This paper presents a new automatic system for detection of pathological brain through magnetic resonance imaging (MRI). The proposed system involves contrast enhancement of input MR images using contrast limited adaptive histogram equalization (CLAHE). Then, the curve like features are computed from the preprocessed MR brain images using fast discrete curvelet transform via unequally-spaced FFT (FDCT-USFFT). Subsequently, a combined technique known as PCA+LDA is employed to derive more discriminative and reduced feature sets. Finally, a novel learning approach dubbed as extreme learning machine with modified sine cosine algorithm (MSCA-ELM) is proposed by combining ELM and MSCA for classification of MR images into two categories: pathological and healthy. A mutation operator is introduced to basic SCA (MSCA). In MSCA-ELM, MSCA is used to optimize the input weights and hidden biases of single-hidden layer feed-forward neural network (SLFN) and an analytical procedure is used to compute the output weights. The proposed scheme is rigorously evaluated on three standard datasets and the results are compared against other competent schemes. The experimental results demonstrate that the proposed scheme outperforms its counterparts in terms of classification accuracy and number of features required. It has also been noticed that MSCA-ELM yields superior performance than conventional learning methods. Hence, the proposed system can effectively recognize pathological brain in real-time and can possibly be installed on medical robots.