[HTML][HTML] Deep into the brain: artificial intelligence in stroke imaging
Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining
increasing interest and is being incorporated into many fields, including medicine. Stroke …
increasing interest and is being incorporated into many fields, including medicine. Stroke …
Radiological images and machine learning: trends, perspectives, and prospects
The application of machine learning to radiological images is an increasingly active
research area that is expected to grow in the next five to ten years. Recent advances in …
research area that is expected to grow in the next five to ten years. Recent advances in …
Big data analysis for brain tumor detection: Deep convolutional neural networks
J Amin, M Sharif, M Yasmin, SL Fernandes - Future Generation Computer …, 2018 - Elsevier
Brain tumor detection is an active area of research in brain image processing. In this work, a
methodology is proposed to segment and classify the brain tumor using magnetic resonance …
methodology is proposed to segment and classify the brain tumor using magnetic resonance …
ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment,
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …
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 …
Evaluating white matter lesion segmentations with refined Sørensen-Dice analysis
The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …
segmentation algorithms. It offers a standardized measure of segmentation accuracy which …
Multi-feature analysis for automated brain stroke classification using weighted Gaussian naïve Bayes classifier
S Jayachitra, A Prasanth - journal of circuits, systems and …, 2021 - World Scientific
In today's world, brain stroke is considered as a life-threatening disease provoked by
undesirable blockage among the arteries feeding the human brain. The timely diagnosis of …
undesirable blockage among the arteries feeding the human brain. The timely diagnosis of …
Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier
Magnetic resonance imaging (MRI) is effectively used for accurate diagnosis of acute
ischemic stroke. This paper presents an automated method based on computer aided …
ischemic stroke. This paper presents an automated method based on computer aided …
Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles
Prediction of state of charge (SOC) is critical to the reliability and durability of battery systems
in electric vehicles. The existing techniques are mostly model-based SOC estimation using …
in electric vehicles. The existing techniques are mostly model-based SOC estimation using …
Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …