Showing papers in "Biocybernetics and Biomedical Engineering in 2020"

TL;DR: An alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work, and the efficacy of proposed method in present need of time is shown.

TL;DR: This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach that obtained the high classification accuracy when compared to other state-of-art methods.

TL;DR: The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19, a pandemic caused by novel coronavirus, from X-ray images.

TL;DR: The study presents a brief comparison of various functional neuroimaging techniques, revealing the excellent Neuroimaging capabilities of EEG signals such as high temporal resolution, inexpensiveness, portability, and non-invasiveness as compared to the other techniques such as positron emission tomography, magnetoencephalogram, functional magnetic resonance imaging, and transcranial magnetic stimulation.

TL;DR: In this article, the authors present the current state-of-the-art of additive manufacturing applications in the biomedical field, especially in tissue engineering, taking into account hydrogels in scaffolds fabrication.

TL;DR: An automated method based on computer aided decision system to detect the ischemic stroke using diffusion-weighted image (DWI) sequence of MR images and efficiently detected the stroke lesions with an accuracy of 93.4% using RF classifier, better than the results of the SVM classifier.

TL;DR: The proposed model is consistent diagnosis model for lung cancer detection using chest CT images using LeNet, AlexNet and VGG-16 deep learning models.

TL;DR: This paper uses magnetic resonance imaging images to train a new hybrid paradigm which consists of a neural autoregressive distribution estimation (NADE) and a convolutional neural network (CNN) and test this model with 3064 T1-weighted contrast-enhanced images with three types of brain tumors.

TL;DR: This review mainly focuses on bone - biodegradable implant interface with due consideration accorded to the mechanical properties, degradation rates and healing process in a standard duration.

TL;DR: In this study, empirical mode decomposition (EMD) based features are demonstrated to capture the speech characteristics of Parkinson's disease and it is demonstrated that the proposed intrinsic mode function cepstral coefficient feature provides superior classification accuracy in both datasets.

TL;DR: A deep learning-based method by using single channel electroencephalogram (EEG) that automatically exploits the time–frequency spectrum of EEG signal, removing the need for manual feature extraction is developed.

TL;DR: It is demonstrated that time-domain statistical characteristics of EEG signals can efficiently discriminate different emotional states and can be used to support the development of real-time EEG-based emotion recognition systems.

TL;DR: A deep learning-based architecture called Residual U-Net with a false-positive removal algorithm for lung CT segmentation is implemented with the suggested that learning from a substantially deeper network with residual units can extract more discriminative feature representation as compared to shallow network for lung segmentation.

TL;DR: This work introduces an optimized deep learning mechanism; named Dolphin-SCA based Deep CNN, to improve the accuracy and to make effective decisions in classification of brain tumor classification.

TL;DR: A combination of minimum average maximum (MAMa) tree and singular value decomposition (SVD) are used to extract the salient features from the voice signals to automatically detect Parkinson's disease using vowels.

TL;DR: Multivariate analysis of the EEG signal for the detection of Schizophrenia condition and five entropy measures measured from the IMF signal showed a significant difference.

TL;DR: The proposed approach based on tunable-Q wavelet transform, entropies, Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) is promising and able to discriminate the epileptic seizure types with satisfactory classification performance.

TL;DR: A wrapper method utilizing the Ant Lion Optimization algorithm is presented that searches for best feature weights and parametric values of Multilayer Neural Network simultaneously, which validates the work which has the potential for becoming an alternative to the other well-known techniques.

TL;DR: The best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG) is explored and experiments showed that SVM has a slight edge over KNN.

TL;DR: A modified U-Net architecture based on residual network is presented and employs periodic shuffling with sub-pixel convolution initialized to convolution nearest neighbour resize and provides state-of-the-art results for retinal lesion segmentation.

TL;DR: An increase of accuracy and a reduction of the number of selected vocal features in PD detection while using the newest and largest public dataset available, as well as reducing the corresponding computational complexity by selecting no more than 20 features.

TL;DR: A stacking-based evolutionary ensemble learning system “NSGA-II-Stacking” for predicting the onset of Type-2 diabetes mellitus (T2DM) within five years is developed and significantly outperforms several individual ML approaches and conventional ensemble approaches.

TL;DR: An attempt has been made to review the available works in the area of medical image processing of blood smear images, highlighting automated detection of leukemia.

TL;DR: The results show that the spectral entropy can provide good separation between different among ECG beats and the proposed method outperforms the recently introduced method for analyzing ECG signals.

TL;DR: In this paper, an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature is presented.

TL;DR: A new method of retinal blood vessel segmentation that is based on a multi-path convolutional neural network, which can be used for computer-based clinical medical image analysis and could effectively suppress noise, ensure continuity after blood vessels segmentation, and provide a feasible new idea for intelligent visual perception of medical images is proposed.

TL;DR: The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal.

TL;DR: This study shows that fused fine-tuned deep features are rather useful in recognizing different TMs and these features can provide a fully automated model with high sensitivity and release a new publicly available TM data set.

TL;DR: A radiomics based deeply supervised U-Net is proposed for both prostate gland and prostate lesion segmentation and effective segmentation of prostate lesions in various stages of prostate cancer is achieved using the proposed framework.

TL;DR: Results when the sampling frequency of the ECG signals is resampled are shown and a new preprocessing stage is proposed that applies Wavelet based on Atomic Functions to eliminate the noise and baseline wander.