Showing papers in "Biocybernetics and Biomedical Engineering in 2019"

TL;DR: The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images and can be readily used in practice for assisting the doctor to diagnose brain tumors in an early stage.

TL;DR: This study demonstrates classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells and provides the comparison of traditional image processing approach and deep learning methods for classification.

TL;DR: A comprehensive survey on the following facets of mental stress detection systems: physiological data collection, role of machine learning in Emotion Detection systems and Stress Detection systems, various evaluation measures, challenges and applications.

TL;DR: In this article, a deep learning model architecture is proposed and evaluated using the MIT-BIH Arrhythmia and Supraventricular Databases and the results obtained show that without applying any complicated data pre-processing or feature engineering methods, both of their models have considerably outperformed the state-of-the-art performance for supraventricular (SVEB) and ventricular (VEB), arrhythmia classifications on the unseen testing dataset (with the F1 score improved from 51.08 to 61.25 for SVEB detection and from

TL;DR: A two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities is proposed and it is confirmed that AC offers enhanced results compared with other segmentation procedures considered in this article.

TL;DR: The findings of this evaluation clearly show that the fuzzy logic methods are vastly used for diagnosis of diseases such as dengue fever, hepatitis and tuberculosis.

TL;DR: Results of feature selection techniques indicate that glucose, age and resistin are found to be most relevant and effective biomarkers for breast cancer prediction.

TL;DR: The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks.

TL;DR: An automatic segmentation method integrating the small kernels two-path convolutional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extraction ability of SK-T PCNN and the joint optimization capability of model are presented respectively.

TL;DR: Experiments show that the pre-trained Convolutional Neural Network model outperforms conventionally used detection systems and provides at least 15% improvement in F-score on other state-of-the-art techniques.

TL;DR: Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.

TL;DR: A new approach for breast histopathological image classification that uses a deep convolution neural network (CNN) with wavelet decomposed images and incorporates multi-scale discriminant features for precise prognostication of class labels is proposed.

TL;DR: The automated toolkit can aid cardiac physicians in the accurate diagnosis of CHF patients to confirm their findings with the authors' system, and it will help to provide timely treatment forCHF patients and save life.

TL;DR: A rapid and automatic method for brain tumour detection and segmentation using multi-sequence magnetic resonance imaging datasets available from BraTS and a novel probabilistic local ternary patterns (PLTP) technique is used to segment the tumour substructures based on the probability density value of histogram bins.

TL;DR: The results of testing the SVM models showed that the features based on the original EEG, α and β waves have got better performances in monitoring the brain network states.

TL;DR: A new multi-channel PCG-based system to classify CAD-affected and normal subjects is proposed, and it does not require any additional reference signal, such as an electrocardiogram (ECG) signal.

TL;DR: Heterogeneous tumor types of different grade levels and tissue structures present in the brain MR slices of three different axes are precisely segmented by the proposed methodology for better visualization of oncologists.

TL;DR: High accuracy characteristic features of toe photoplethysmogram are used for the detection of type-2 DM using support vector machine (SVM) and it is hoped that this work will help the clinician in screening of diabetes and adopting suitable treatment plan for preventing end organ damage.

TL;DR: Some critical issues related to the medical diagnosis and prognosis such as uncertainties in themedical domain, problems in the medical data especially dealing with time-stamped (temporal) data, and knowledge acquisition are discussed.

TL;DR: This study proposes the development of a passive exoskeleton that enhances mobility during stance and walking through an unpowered seat mechanism that produces an upward force on the pelvis of the user.

TL;DR: A systemic investigation of works performed with window based FIR filter and SG filter found very convincing results to use SG filter with specific conditions and proposed a recommendation which indicates the best conditions of the SG filter to remove physiological noises from the fNIRS signals.

TL;DR: A robust system that generates control command using only one type of asynchronous eye activity (voluntary eye blink) to navigate the wheelchair without a need of graphical user interface to provide a user-friendly, cost-effective and reliable HMI.

TL;DR: The outcome of the proposed continuous terrain identification method shows a pronounced potential in efficient lower limb prosthesis control and significantly improved the identification accuracy.

TL;DR: It can be concluded that the proposed hybrid CAD system design could be used as a second opinion tool in clinical setting with optimal performance for classification of benign and malignant breast tumors with a classification accuracy of 96.0%.

TL;DR: In this paper, 84 finite element (FE) models of the pedicle screw were generated having 7 pitch lengths, 3 major diameters, 2 thread profiles and 2 geometries.

TL;DR: The strength of the proposed algorithm is proven by comparing it with the state-of-the-art techniques by means of evaluation parameters like mean squared error (MSE), peak signal to noise ratio (PSNR), sensitivity, specificity, etc.,

TL;DR: The results of both quantitative and qualitative assessment by the radiologist indicate that the DPAD despeckle filtering algorithm yields more clinically acceptable images and results in better segmentation of benign and malignant tumours from breast ultrasound images.

TL;DR: Performance results over three independent datasets suggest that the proposed technique can provide accurate and reliable HR information using the PPG signal recorded from the patients suffering from dissimilar problems.

TL;DR: It was concluded that the most critical regions experiencing low circumferential strain and low WSS were located proximal to the plaque throat, and the effects of these parameters intensified by stenosis degree.

TL;DR: The preliminary results show that the exoskeleton is able to reduce the activation of the calf muscles on the limb wearing the device, however, a decrease in ankle joint range of motion is noted in the limb with thedevice, and, to a much lesser extent the leg without theExoskeleton.