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[HTML][HTML] A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe
In our study, we introduce a novel hybrid ensemble model that synergistically combines
LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations …
LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations …
Harris hawks optimization for COVID-19 diagnosis based on multi-threshold image segmentation
Digital image processing techniques and algorithms have become a great tool to support
medical experts in identifying, studying, diagnosing certain diseases. Image segmentation …
medical experts in identifying, studying, diagnosing certain diseases. Image segmentation …
Monarch butterfly optimization algorithm for computed tomography image segmentation
In the medical field, image segmentation provides important information for surgical
planning and registration, and thus demands accurate segmentation. In order to improve the …
planning and registration, and thus demands accurate segmentation. In order to improve the …
Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation
We present a framework for robustly estimating registration between a 3D volume image
and a 2D projection image and evaluate its precision and robustness in spine interventions …
and a 2D projection image and evaluate its precision and robustness in spine interventions …
Enhancing the security of exchanging and storing DICOM medical images on the cloud
O Dorgham, B Al-Rahamneh, A Almomani… - International Journal of …, 2018 - igi-global.com
Medical image information can be exchanged remotely through cloud-based medical
imaging services. Digital Imaging and Communication in Medicine (DICOM) is considered to …
imaging services. Digital Imaging and Communication in Medicine (DICOM) is considered to …
U-NetCTS: U-Net deep neural network for fully automatic segmentation of 3D CT DICOM volume
The accurate segmentation of computed tomography (CT) scan volume is an essential step
in radiomic analysis as well as in develo** advanced surgical planning techniques with …
in radiomic analysis as well as in develo** advanced surgical planning techniques with …
An Ensemble approach to question classification: Integrating electra transformer, GloVe, and LSTM
Natural Language Processing (NLP) has emerged as a crucial technology for understanding
and generating human language, playing an essential role in tasks such as machine …
and generating human language, playing an essential role in tasks such as machine …
Performance Evaluation of Swin Vision Transformer Model using Gradient Accumulation Optimization Technique
Abstract Vision Transformers (ViTs) have emerged as a promising approach for visual
recognition tasks, revolutionizing the field by leveraging the power of transformer-based …
recognition tasks, revolutionizing the field by leveraging the power of transformer-based …
[HTML][HTML] Automatic body segmentation for accelerated rendering of digitally reconstructed radiograph images
The rendering of digitally reconstructed radiograph (DRR) images involves creating a digital
reconstruction of an image made by a three-dimensional (3D) imaging system, such as a …
reconstruction of an image made by a three-dimensional (3D) imaging system, such as a …
Smart system for prediction of accurate surface electromyography signals using an artificial neural network
O Dorgham, I Al-Mherat, J Al-Shaer, S Bani-Ahmad… - Future Internet, 2019 - mdpi.com
Bioelectric signals are used to measure electrical potential, but there are different types of
signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and …
signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and …