[HTML][HTML] Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor
Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment
options, while accurate classification and segmentation of brain tumors are vital for …
options, while accurate classification and segmentation of brain tumors are vital for …
DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction using IOT Network
A Yashudas, D Gupta, GC Prashant, A Dua… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
The Internet of Things (IoTs)-based remote healthcare applications provide fast and
preventative medical services to the patients at risk. However, predicting heart disease is a …
preventative medical services to the patients at risk. However, predicting heart disease is a …
Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image-and Signal-Based Studies
Generative Adversarial Networks (GANs) have emerged as a powerful tool in artificial
intelligence, particularly for unsupervised learning. This systematic review analyzes GAN …
intelligence, particularly for unsupervised learning. This systematic review analyzes GAN …
Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the
potential to transform diagnosis and estimate the prognosis of not only cardiac but …
potential to transform diagnosis and estimate the prognosis of not only cardiac but …
Advancing cardiac diagnostics: Exceptional accuracy in abnormal ECG signal classification with cascading deep learning and explainability analysis
Arrhythmias, cardiac rhythm disorders, demand precise diagnosis for effective treatment
planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation …
planning, emphasizing the crucial role of electrocardiogram (ECG) signal interpretation …
Open-world electrocardiogram classification via domain knowledge-driven contrastive learning
Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for
assisting disease diagnosis and has received much attention in research. The success of …
assisting disease diagnosis and has received much attention in research. The success of …
[HTML][HTML] An electrocardiogram signal classification using a hybrid machine learning and deep learning approach
An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the
heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which …
heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which …
[HTML][HTML] GAN-SkipNet: a solution for data imbalance in cardiac arrhythmia detection using electrocardiogram signals from a benchmark dataset
Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual
analysis of ECG signals is challenging due to the complex task of identifying and …
analysis of ECG signals is challenging due to the complex task of identifying and …
PPG-based continuous BP waveform estimation using polarized attention-guided conditional adversarial learning model
C Ma, Y Xu, P Zhang, F Song, Y Sun… - IEEE journal of …, 2023 - ieeexplore.ieee.org
The blood pressure (BP) waveform is a vital source of physiological and pathological
information concerning the cardiovascular system. This study proposes a novel attention …
information concerning the cardiovascular system. This study proposes a novel attention …
HRIDM: Hybrid Residual/Inception-Based Deeper Model for Arrhythmia Detection from Large Sets of 12-Lead ECG Recordings
Heart diseases such as cardiovascular and myocardial infarction are the foremost reasons
of death in the world. The timely, accurate, and effective prediction of heart diseases is …
of death in the world. The timely, accurate, and effective prediction of heart diseases is …