It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications; hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5×5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance.

Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system based on particle swarm optimization (PSO) and artificial bee colony (ABC), with the aim of distinguishing abnormal brains from normal brains in MRI scanning. The proposed method used stationary wavelet transform (SWT) to extract features from MR brain images. SWT is translation-invariant and performed well even the image suffered from slight translation. Next, principal component analysis (PCA) was harnessed to reduce the SWT coefficients. Based on three different hybridization methods of PSO and ABC, we proposed three new variants of feed-forward neural network (FNN), consisting of IABAP-FNN, ABC-SPSO-FNN, and HPA-FNN. The 10 runs of K-fold cross validation result showed the proposed HPA-FNN was superior to not only other two proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the method achieved perfect classification on Dataset-66 and Dataset-160. For Dataset-255, the 10 repetition achieved average sensitivity of 99.37%, average specificity of 100.00%, average precision of 100.00%, and average accuracy of 99.45%. The offline learning cost 219.077 s for Dataset-255, and merely 0.016 s for online prediction. Thus, the proposed SWT 1 PCA 1 HPA-FNN method excelled existing methods. It can be applied to practical use.

Near-infrared (NIR) light has stronger penetration capability than visible light due to its long wavelength, thus being less scattered by particles in the air. This makes it desirable for image dehazing to unveil details of distant objects in landscape photographs. In this paper, we propose an improved image dehazing scheme using a pair of color and NIR images, which effectively estimates the airlight color and transfers details from the NIR. A two-stage dehazing method is proposed by exploiting the dissimilarity between RGB and NIR for airlight color estimation, followed by a dehazing procedure through an optimization framework. Experiments on captured haze images show that our method can achieve substantial improvements on the detail recovery and the color distribution over the existing image dehazing algorithms.

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods; moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods.

No abstract

This study aimed to quantify whether there is association between music-based movement therapy and motor dysfunction in patients with Parkinson's disease, and, if so, whether music-based movement therapy can be used as first-line non-pharmacological treatment. To conduct a systematic review and meta-analysis of clinical trials that examined the effect of music-based movement therapy on patient-relevant and disease-specific outcomes. Comprehensive literature was searched of PubMed, EMbase, and the Cochrane Library from inception to November 2016. Randomized controlled trial of patients with Parkinson's disease was searched to identify trials comparing music-based movement therapy with no music care. A total of 8 studies (11 analyses, 241 subjects) were included; all of them had acceptable quality by PEDro scale score. Studies based on any type of Parkinson's disease patients were combined and subgroup analyzed. Compared with the control group, the SMD of Berg Balance Scale score was 0.85(0.46 to 1.25), -0.60 (-0.98 to -0.22) in Parkinson Disease Questionnaire-39 summary index, -0.90s (-1.56 to -0.23) in Time Up and Go text, and -0.43 (-1.11 to 0.25) in Unified Parkinson's Disease Rating Scale Motor Subscale 3 as instrument methods for motor function. Secondary outcomes included cognitive function and quality of life. There was positive evidence to support the use of music-based movement therapy on treatment of motor function; there was neutral evidence to support the use of music for the treatment of cognitive function quality of life.

In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

In modern molecular biology, the hotspots and difficulties of this field are identifying characteristic genes from gene expression data. Traditional reconstruction-error-minimization model principal component analysis (PCA) as a matrix decomposition method uses quadratic error function, which is known sensitive to outliers and noise. Hence, it is necessary to learn a good PCA method when outliers and noise exist. In this paper, we develop a novel PCA method enforcing P-norm on error function and graph-Laplacian regularization term for matrix decomposition problem, which is called as PgLPCA. The heart of the method designing for reducing outliers and noise is a new error function based on non-convex proximal P-norm. Besides, Laplacian regularization term is used to find the internal geometric structure in the data representation. To solve the minimization problem, we develop an efficient optimization algorithm based on the augmented Lagrange multiplier method. This method is used to select characteristic genes and cluster the samples from explosive biological data, which has higher accuracy than compared methods.