The deep learning applications in IoT-based bio-and medical informatics: a systematic literature review
Z Amiri, A Heidari, NJ Navimipour… - Neural Computing and …, 2024 - Springer
Nowadays, machine learning (ML) has attained a high level of achievement in many
contexts. Considering the significance of ML in medical and bioinformatics owing to its …
contexts. Considering the significance of ML in medical and bioinformatics owing to its …
[HTML][HTML] Multimodal medical image fusion towards future research: A review
Medical imaging has been widely used to diagnose various disorders over the past 20
years. Primary challenges in medicine include accurate disease identification and improved …
years. Primary challenges in medicine include accurate disease identification and improved …
COVID-ECG-RSNet: COVID-19 classification from ECG images using swish-based improved ResNet model
Millions of humans worldwide have been impacted by consecutive COVID-19 waves during
the past three years. The correct treatment for COVID-19 has not yet been identified because …
the past three years. The correct treatment for COVID-19 has not yet been identified because …
Comparative review on traditional and deep learning methods for medical image segmentation
Medical image segmentation is a vital task in medical imaging, aiming to extract meaningful
and precise information from images. While traditional methods have been extensively used …
and precise information from images. While traditional methods have been extensively used …
Residual encoder-decoder based architecture for medical image denoising
High-resolution computed tomography (CT) scans require high doses of X-rays, posing
potential health risks to patients, including genetic damage and cancer. Conversely, low …
potential health risks to patients, including genetic damage and cancer. Conversely, low …
[HTML][HTML] X-RCRNet: An explainable deep-learning network for COVID-19 detection using ECG beat signals
Wearable systems measuring human physiological indicators with integrated sensors and
supervised learning-based medical image analysis (eg ECG, X-ray, CT or ultrasound …
supervised learning-based medical image analysis (eg ECG, X-ray, CT or ultrasound …
Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model
M Kolhar, AM Al Rajeh - Scientific Reports, 2024 - nature.com
Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them.
Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle …
Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle …
Thoracic-net: Explainable artificial intelligence (XAI) based few shots learning feature fusion technique for multi-classifying thoracic diseases using medical imaging
Abstract Background & Need: The early detection of thoracic diseases and COVID-19
(coronavirus disease) significantly limits propagation and increases therapeutic outcomes …
(coronavirus disease) significantly limits propagation and increases therapeutic outcomes …
Multi-view X-ray Image Synthesis with Multiple Domain Disentanglement from CT Scans
X-ray images play a vital role in the intraoperative processes due to their high resolution and
fast imaging speed and greatly promote the subsequent segmentation, registration and …
fast imaging speed and greatly promote the subsequent segmentation, registration and …
Severity wise COVID-19 X-ray image augmentation and classification using structure similarity
P Dwivedi, S Padhi, S Chakraborty… - Multimedia Tools and …, 2024 - Springer
Deep Learning models are widely used to address COVID-19 challenges, but they require a
large number of training samples. X-ray images of COVID-19 patients are amongst the …
large number of training samples. X-ray images of COVID-19 patients are amongst the …