Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering
performance in a variety of computer vision problems, such as visual object recognition …
performance in a variety of computer vision problems, such as visual object recognition …
[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 …
Brain tumor detection using fusion of hand crafted and deep learning features
T Saba, AS Mohamed, M El-Affendi, J Amin… - Cognitive Systems …, 2020 - Elsevier
The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by
damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor …
damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor …
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …
methods on various public, medical-image challenge datasets, particularly on metrics …
[HTML][HTML] Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural
Network for the challenging task of brain lesion segmentation. The devised architecture is …
Network for the challenging task of brain lesion segmentation. The devised architecture is …
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 …
Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial
Background Trials of fluoxetine for recovery after stroke report conflicting results. The
Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral …
Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral …
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 …
Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer's disease
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, eg, mild cognitive
impairment (MCI), is essential for timely treatment or possible intervention to slow down AD …
impairment (MCI), is essential for timely treatment or possible intervention to slow down AD …
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 …