Shuqiao Liu, Junliang Li, Xiaojie Li^*

Journal on Internet of Things, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jiot.2020.09797

Abstract In the deep learning field, “Capsule” structure aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classification network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the difference between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and the normal image is not… More >

Lan Yang¹, Shun Qi^2,3,#, Chen Qiao^1,*, Yanmei Kang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 215-237, 2020, DOI:10.32604/cmes.2020.010796

Abstract Schizophrenia (SZ) is one of the most common mental diseases. Its main characteristics are abnormal social behavior and inability to correctly understand real things. In recent years, the magnetic resonance imaging (MRI) technique has been popularly utilized to study SZ. However, it is still a great challenge to reveal the essential information contained in the MRI data. In this paper, we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data, in which biomarkers represent both abnormal brain functional connectivity and… More >

Machiraju Jayalakshmi^{1, *}, S. Nagaraja Rao²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710

Abstract 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)… More >

M. Uma^a,c, T. Sheela^b

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 747-757, 2018, DOI:10.1080/10798587.2017.1332804

Abstract Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects were asked to choose the… More >

Mohamed Abbas^1,3,*, Ali Algahtani^2,6, Amir Kessentini^2,4,7, Hassen Loukil^1,5, Muneer Parayangat¹, Thafasal Ijyas¹, Abdul Wase Mohammed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 683-701, 2020, DOI:10.32604/cmes.2020.010304

Abstract Every new generation of antennas is characterized by increased accuracy and faster transmission speeds. However, patch antennas have been known to damage human health. This type of antenna sends out electromagnetic waves that increase the temperature of the human head and prevent nerve strands from functioning properly. This paper examines the effect of the communication between the patch antenna and the brain on the head’s temperature by developing a hypothetical multi-input model that achieves more accurate results. These inputs are an individual’s blood and tissue, and the emission power of the antenna. These forces depend on the permeability and conductivity… More >

A. Renugambal^{1, *}, K. Selva Bhuvaneswari²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 681-700, 2020, DOI:10.32604/cmc.2020.09519

Abstract In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and… More >

Ravi Samikannu^{1, *}, Rohini Ravi², Sivaram Murugan³, Bakary Diarra⁴

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1133-1142, 2020, DOI:10.32604/cmc.2020.08578

Abstract The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain… More >

Jan Hinnerk Hansen¹, Lydia Kissner¹, Jana Logoteta¹, Olaf Jung¹, Peter Dütschke², Tim Attmann³, Jens Scheewe³, Hans‐Heiner Kramer^1,4

Congenital Heart Disease, Vol.14, No.3, pp. 427-437, 2019, DOI:10.1111/chd.12741

Abstract Objectives: Neonates and infants undergoing surgery for congenital heart disease are at risk for developmental impairment. Hypoxic‐ischemic brain injury might be one contributing factor. We aimed to investigate the perioperative release of the astro‐ cyte protein S100B and its relation to cerebral oxygenation.
Methods: Serum S100B was measured before and 0, 12, 24, and 48 hours after sur‐ gery. Cerebral oxygen saturation was derived by near‐infrared spectroscopy. S100B reference values based on preoperative samples; concentrations above the 75th per‐ centile were defined as elevated. Patients with elevated S100B at 24 or 48 hours were compared to cases with S100B in… More >

Zhuqing Jiao^{1, *}, Yixin Ji¹, Tingxuan Jiao¹, Shuihua Wang^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 845-871, 2020, DOI:10.32604/cmes.2020.08999

Abstract Currently, functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders. If one brain disease just manifests as some cognitive dysfunction, it means that the disease may affect some local connectivity in the brain functional network. That is, there are functional abnormalities in the sub-network. Therefore, it is crucial to accurately identify them in pathological diagnosis. To solve these problems, we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization (GNMF). The dynamic functional networks of normal subjects and early mild cognitive impairment (eMCI) subjects were vectorized and the functional connection… More >

Bin Yang^1,2,3,*, Hao Sun¹, Aiyuan Wang¹, Qun Wang²

Molecular & Cellular Biomechanics, Vol.17, No.1, pp. 49-62, 2020, DOI:10.32604/mcb.2019.07534

Abstract In order to predict and evaluate injury mechanism and biomechanical response of the facial impact on head injury in a crash accident. With the combined modern medical imaging technologies, namely computed tomography (CT) and magnetic resonance imaging (MRI), both geometric and finite element (FE) models for human head-neck with detailed cranio-facial structure were developed. The cadaveric head impact tests were conducted to validate the headneck finite element model. The intracranial pressure, skull dynamic response and skull-brain relative displacement of the whole head-neck model were compared with experimental data. Nine typical cases of facial traffic accidents were simulated, with the individual… More >