Medical and Biological Engineering and Computing This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method
AbstractMedical image processing has become increasingly important in recent years, particularly in the field of microscopic cell imaging. However, accurately counting the number of cells in an image can be a challenging task due to the significant variations in cell size and shape. To tackle this problem, many existing methods rely on deep learning techniques, such as convolutional neural networks (CNNs), to count cells in an image or use regression counting methods to learn the similarities between an input image and a predicted cell image density map. In this paper, we propose a novel approach to monitor the cell counti...
Source: Medical and Biological Engineering and Computing - August 3, 2023 Category: Biomedical Engineering Source Type: research

An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection
AbstractIn this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) s...
Source: Medical and Biological Engineering and Computing - August 2, 2023 Category: Biomedical Engineering Source Type: research

Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture
AbstractPrediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data –based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages — stages 1, 2, 3, and 4 [2]. While machine learning models have previously bee n explored to perform stage classification with limited success, multi-class stage classifi...
Source: Medical and Biological Engineering and Computing - August 2, 2023 Category: Biomedical Engineering Source Type: research

Automated detection and localization of pericardial effusion from point-of-care cardiac ultrasound examination
AbstractFocused Assessment with Sonography in Trauma (FAST) exam is the standard of care for pericardial and abdominal free fluid detection in emergency medicine. Despite its life saving potential, FAST is underutilized due to requiring clinicians with appropriate training and practice. To aid ultrasound interpretation, the role of artificial intelligence has been studied, while leaving room for improvement in localization information and computation time. The purpose of this study was to develop and test a deep learning approach to rapidly and accurately identify both the presence and location of pericardial effusion on p...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging
This study aims to propose a deep learning based automatic evaluation strategy for DWI-ASPECTS to serve as a reference for clinicians in urgent decision making for endovascular thrombectomy. Ten ASPECTS regions are extracted from the DWI series to train the independent classification network for each region, the accurate training labels of which are confirmed by neuroradiologists. Two classical convolutional neural networks (VGG-16 and ResNet-50) are validated. Subsequently, the innovative CBAM-VGG is designed to improve the accurate scoring of four small-volume DWI-ASPECTS regions, including caudate nucleus, lenticular nu...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

A novel Gateaux derivatives with efficient DCNN-Resunet method for segmenting multi-class brain tumor
AbstractIn hospitals and pathology, observing the features and locations of brain tumors in Magnetic Resonance Images (MRI) is a crucial task for assisting medical professionals in both treatment and diagnosis. The multi-class information about the brain tumor is often obtained from the patient ’s MRI dataset. However, this information may vary in different shapes and sizes for various brain tumors, making it difficult to detect their locations in the brain. To resolve these issues, a novel customized Deep Convolution Neural Network (DCNN) based Residual-Unet (ResUnet) model with Transfe r Learning (TL) is proposed for p...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

TPFR-Net: U-shaped model for lung nodule segmentation based on transformer pooling and dual-attention feature reorganization
AbstractAccurate segmentation of lung nodules is the key to diagnosing the lesion type of lung nodule. The complex boundaries of lung nodules and the visual similarity to surrounding tissues make precise segmentation of lung nodules challenging. Traditional CNN based lung nodule segmentation models focus on extracting local features from neighboring pixels and ignore global contextual information, which is prone to incomplete segmentation of lung nodule boundaries. In the U-shaped encoder-decoder structure, variations of image resolution caused by up-sampling and down-sampling result in the loss of feature information, whi...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Fast soft-tissue deformations coupled with mixed reality toward the next-generation childbirth training simulator
AbstractHigh-quality gynecologist and midwife training is particularly relevant to limit medical complications and reduce maternal and fetal morbimortalities. Physical and virtual training simulators have been developed. However, physical simulators offer a simplified model and limited visualization of the childbirth process, while virtual simulators still lack a realistic interactive system and are generally limited to imposed predefined gestures. Objective performance assessment based on the simulation numerical outcomes is still not at hand. In the present work, we developed  a virtual childbirth simulator based on the...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images
The objective of this study is to systematically review the literature to study various techniques used for the automatic segmentation of the human upper airway regions in volumetric images. PRISMA guidelines were followed to conduct the systematic review. Four online databases Scopus, Google Scholar, PubMed, and JURN were used for the searching of the relevant papers. The relevant papers were shortlisted using inclusion and exclusion eligibility criteria. Three review questions were made and explored to find their answers. The best technique among all the literature studies based on the Dice coefficient and precision was ...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Electron beam detection in radiotherapy using a capacitor dosimeter equipped with a silicon photodiode
In this study, a newly developed capacitor dosimeter was evaluated using electron beams commonly utilized in radiotherapy. The capacitor dosimeter comprised a silicon photodiode, 0.47- μF capacitor, and dedicated terminal (dock). Before electron beam irradiation, the dosimeter was charged using the dock. The doses were measured without using a cable by reducing the charging voltages using the currents from the photodiode during irradiation. A commercially available parallel-plane -type ionization chamber and solid–water phantom were used for dose calibration with an electron energy of 6 MeV. In addition, the depth dose...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Posture monitoring in healthcare: a systematic mapping study and taxonomy
This article also proposes a taxonomy, showing the most used technologies and algorithms for improving posture, besides the posture-monitoring hierarchy classifying into three important branches: (a) Data Collect; (b) Data Transmission; and (c) Data Analysis.Graphical Abstract (Source: Medical and Biological Engineering and Computing)
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Automatic severity grade classification of diabetic retinopathy using deformable ladder Bi attention U-net and deep adaptive CNN
AbstractLong-term exposure to diabetes mellitus leads to the formation of diabetic retinopathy (DR), which can cause vision loss in working-age adults. Early stage diagnosis of DR is highly essential for preventing vision loss and preserving vision in people with diabetes. The motivation behind the severity grade classification of DR is to develop an automated system that can assist ophthalmologists and healthcare professionals in the diagnosis and management of DR. However, existing methods suffer from variability in image quality, similar structures of the normal and lesion regions, high dimensional features, variability...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images
AbstractEncoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the ne...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

Comparison of four machine learning algorithms for a pre-impact fall detection system
AbstractIn recent years, real-time health monitoring using wearable sensors has been an active area of research. This paper presents an efficient and low-cost fall detection system based on a pair of shoes equipped with inertial sensors and plantar pressure sensors. In addition, four machine learning algorithms (KNN, SVM, RF, and BP neural network) are compared in terms of their detection performance and suitability for pre-impact fall detection. The results show that KNN and BP neural network outperformed the other two algorithms, where KNN had 98.8% sensitivity, 99.8% specificity, and 99.7% accuracy, and BP neural networ...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research

The correlation between upper body grip strength and resting-state EEG network
In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p <  0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands,...
Source: Medical and Biological Engineering and Computing - July 13, 2023 Category: Biomedical Engineering Source Type: research