Showing papers in "Biomedical Signal Processing and Control in 2022"

TL;DR: In this paper, a new method for detecting COVID-19 and pneumonia using chest X-ray images was proposed, which can be described as a three-step process and achieved the highest testing classification accuracy of 96.6% using the VGG-19 model associated with the binary robust invariant scalable key-points (BRISK) algorithm.

TL;DR: In this article , a new method for detecting COVID-19 and pneumonia using chest X-ray images was proposed, which can be described as a three-step process and achieved the highest testing classification accuracy of 96.6% using the VGG-19 model associated with the binary robust invariant scalable key-points (BRISK) algorithm.

TL;DR: In this article, the authors used the Gaussian pyramid to improve the simple ORB-oriented algorithm, which is more suitable for minimally invasive surgery endoscopic image mosaic through theoretical analysis and experimental verification.

TL;DR: In this paper , the authors used the Gaussian pyramid to improve the simple ORB-oriented algorithm, which has invariability, good robustness in scale change and rotation change, high registration accuracy, and stitching speed is about 10 times that of SIFT.

TL;DR: In this article , a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced, which is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset.

TL;DR: In this paper , a 3D reconstruction framework combined with stereo vision and Shape from Shading (SFS) was proposed to improve endoscopic imaging accuracy and reduce the difficulty of minimally invasive surgery.

TL;DR: An efficient multi-scale convolutional neural network (MS-CNN) which can extract the distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification is proposed.

TL;DR: In this paper, a deep recurrent neural network-based framework is presented to detect depression and to predict its severity level from speech, which has several advantages (fastness, non-invasiveness, and non-intrusion) which makes it convenient for real-time applications.

TL;DR: In this paper , a deep recurrent neural network-based framework is presented to detect depression and to predict its severity level from speech, which has several advantages such as fastness, non-invasiveness, and non-intrusion.

TL;DR: In this article, a novel method has been presented for improving the screening and classification of COVID-19 patients based on their chest X-ray (CXR) images. But this method eliminates the severe dependence of the deep learning models on large datasets and the deep features extracted from them.

TL;DR: In this paper , a novel method has been presented for improving the screening and classification of COVID-19 patients based on their chest X-ray (CXR) images. But this method eliminates the severe dependence of the deep learning models on large datasets and the deep features extracted from them.

TL;DR: In this paper , a multi-scale convolutional neural network (MS-CNN) was proposed to extract distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification.

TL;DR: A review of the state-of-the-art research on machine learning techniques used for detection and classification of Alzheimer's disease with a focus on neuroimaging and primarily journal articles published since 2016 can be found in this article.

TL;DR: A review of the state-of-the-art research on machine learning techniques used for detection and classification of Alzheimer's disease with a focus on neuroimaging and primarily journal articles published since 2016 can be found in this paper .

TL;DR: In this article, the authors proposed a method based on automated extraction of time-frequency cough features and selection of the more significant ones to be used to diagnose COVID-19 using a supervised machine-learning algorithm.

TL;DR: In this article , the authors proposed a method based on automated extraction of time-frequency cough features and selection of the more significant ones to be used to diagnose COVID-19 using a supervised machine-learning algorithm.

TL;DR: In this article , the authors proposed a novel detection and classification approach (DCCNet) for quick diagnosis of COVID-19 using chest X-ray images of patients using CNN and histogram of oriented gradients (HOG).

TL;DR: Zhang et al. as discussed by the authors proposed a pair-based Siamese Convolutional Neural Network (SCNN) abbreviated ST-AMCNN to realize the idea of pose-guided matching on the eight-section brocade dataset.

TL;DR: Zhang et al. as discussed by the authors proposed a pair-based Siamese Convolutional Neural Network (SCNN) to realize the idea of pose-guided matching on the eight-section brocade dataset which is one of the most representative traditional rehabilitation exercises in China.

TL;DR: Exhaustive simulation results on mammograms dataset, namely, MIAS, DDSM, and INbreast, as well as ultrasound datasets, depict that the suggested model outperforms the recent state-of-the-art schemes.

TL;DR: HyOPTXg as discussed by the authors used the XGBoost and OPTUNA techniques to identify cardiovascular disease using data pre-processing and hyper-parameter tuning techniques and achieved better results than other authors' models.

TL;DR: In this article , an improved version of the EO that combines the standard operators with the dimension learning hunting (DLH) is introduced, which is tested over the CEC 2020 benchmark functions.

TL;DR: In this paper, three pre-trained networks, such as GoogLeNet, AlexNet and ResNet-18 are used in this classification which are trained and tested with 6000 images collected from ADNI database.

TL;DR: In this article , three pre-trained networks, namely GoogLeNet, AlexNet and ResNet-18, are used in this classification and the classification performance of these three networks is analyzed with the help of confusion matrix and its parameters.

TL;DR: In this paper , an exemplar pyramid deep feature extraction-based method has been proposed for the detection of cervical cancer using Pap-smear images for early diagnosis/detection is very important for the treatment of cancer.

TL;DR: In this paper , the EEG signals are first decomposed in sub-bands using empirical wavelet transform (EWT) based on the Fourier Bessel series expansion (FBSE-EWT).

TL;DR: A semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segment masses on dynamic contrast-enhanced MRI of the breast, and is confirmed to outperform the competing literature methods.

TL;DR: In this paper, the EEG signals are first decomposed in sub-bands using empirical wavelet transform (EWT) based on the Fourier Bessel series expansion (FBSE) which is termed as FBSE-EWT.

TL;DR: Wang et al. as discussed by the authors optimized a new network layer design based on LSTM to obtain the autoencoder structure, which can cooperate with the ECG preprocessing process designed by them to obtain better arrhythmia classification effect.

TL;DR: Wang et al. as mentioned in this paper optimized a new network layer design based on LSTM to obtain the autoencoder structure, which can cooperate with the ECG preprocessing process designed by them to obtain better arrhythmia classification effect.