A review on computer aided diagnosis of acute brain stroke
Amongst the most common causes of death globally, stroke is one of top three affecting over
100 million people worldwide annually. There are two classes of stroke, namely ischemic …
100 million people worldwide annually. There are two classes of stroke, namely ischemic …
Brain tumor detection: a long short-term memory (LSTM)-based learning model
To overcome the problems of automated brain tumor classification, a novel approach is
proposed based on long short-term memory (LSTM) model using magnetic resonance …
proposed based on long short-term memory (LSTM) model using magnetic resonance …
Convolutional neural network with batch normalization for glioma and stroke lesion detection using MRI
Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In
this research, deep learning model is presented for glioma and stroke lesion detection. The …
this research, deep learning model is presented for glioma and stroke lesion detection. The …
A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network
The principle restorative step in the treatment of ischemic stroke depends on how fast the
lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as …
lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as …
Quality assessment of stroke radiomics studies: Promoting clinical application
B Sohn, SY Won - European Journal of Radiology, 2023 - Elsevier
Purpose To evaluate the quality of radiomics studies on stroke using a radiomics quality
score (RQS), Minimum Information for Medial AI reporting (MINIMAR) and Transparent …
score (RQS), Minimum Information for Medial AI reporting (MINIMAR) and Transparent …
Identification of invisible ischemic stroke in noncontrast CT based on novel two‐stage convolutional neural network model
G Wu, X Chen, J Lin, Y Wang, J Yu - Medical Physics, 2021 - Wiley Online Library
Purpose Early identification of ischemic stroke lesion regions plays a vital role in its
treatments like thrombolytic therapy and patients' recovery. Noncontrast computed …
treatments like thrombolytic therapy and patients' recovery. Noncontrast computed …
Artificial intelligence applications in acute ischemic stroke
Y Cui, D Han, R Fan, Y Xiao, L Fan, S Liu - Chinese Journal of Academic …, 2023 - Springer
Acute ischemic stroke (AIS) accounts for more than half of stroke cases and results in severe
outcomes from physical disabilities, mental disorders to death. Timely treatment could save …
outcomes from physical disabilities, mental disorders to death. Timely treatment could save …
Intracranial hemorrhage subtype classification using learned fully connected separable convolutional network
In recent decades, intracranial hemorrhage detection from computed tomography (CT)
scans has gained considerable attention among researchers in the medical community. The …
scans has gained considerable attention among researchers in the medical community. The …
A self-adaptive monarch butterfly optimization (MBO) algorithm based improved deep forest neural network model for detecting and classifying brain stroke lesions
SB Melingi, RK Mojjada, C Tamizhselvan… - Research on Biomedical …, 2022 - Springer
Purpose The objective function of this work is to detect and classify the brain stroke lesions
within the short period by increasing the accuracy with efficiency and decreasing the …
within the short period by increasing the accuracy with efficiency and decreasing the …
Ischemic Stroke Classification Using VGG-16 Convolutional Neural Networks: A Study on Moroccan MRI Scans.
This study presents a comprehensive exploration of deep learning models for precise brain
ischemic stroke classification using medical data from Morocco. Following the OSEMN …
ischemic stroke classification using medical data from Morocco. Following the OSEMN …