A Review on a Deep Learning Perspective in Brain Cancer Classification

Cancers, Journal Year: 2019, Volume and Issue: 11(1), P. 111 - 111

Published: Jan. 18, 2019

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in Asian continent. It of critical importance be detected earlier so many these lives can saved. Cancer grading an important aspect for targeted therapy. As diagnosis highly invasive, time consuming and expensive, there immediate requirement develop a non-invasive, cost-effective efficient tools characterization grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well other modalities, are fast safer methods tumor detection. In this paper, we tried summarize pathophysiology cancer, modalities automatic computer assisted machine deep learning paradigm. Another objective paper find current issues existing engineering also project future Further, have highlighted relationship between disorders like stroke, Alzheimer’s, Parkinson’s, Wilson’s disease, leukoriaosis, neurological context

Language: Английский

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Multiple system atrophy

International review of neurobiology, Journal Year: 2019, Volume and Issue: unknown, P. 137 - 192

Published: Jan. 1, 2019

Language: Английский

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An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders

Nature Reviews Neurology, Journal Year: 2020, Volume and Issue: 16(5), P. 265 - 284

Published: April 22, 2020

Language: Английский

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Multiple system atrophy

Nature Reviews Disease Primers, Journal Year: 2022, Volume and Issue: 8(1)

Published: Aug. 25, 2022

Language: Английский

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Biomarkers for Parkinson’s Disease: How Good Are They?

Neuroscience Bulletin, Journal Year: 2019, Volume and Issue: 36(2), P. 183 - 194

Published: Oct. 23, 2019

Language: Английский

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A new MR imaging index for differentiation of progressive supranuclear palsy-parkinsonism from Parkinson's disease

Parkinsonism & Related Disorders, Journal Year: 2018, Volume and Issue: 54, P. 3 - 8

Published: July 25, 2018

Language: Английский

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Establishing a framework for neuropathological correlates and glymphatic system functioning in Parkinson’s disease

Neuroscience & Biobehavioral Reviews, Journal Year: 2019, Volume and Issue: 103, P. 305 - 315

Published: May 24, 2019

Language: Английский

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A clinical-anatomical signature of Parkinson's disease identified with partial least squares and magnetic resonance imaging

NeuroImage, Journal Year: 2017, Volume and Issue: 190, P. 69 - 78

Published: Dec. 19, 2017

Language: Английский

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Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes

Radiology, Journal Year: 2021, Volume and Issue: 300(2), P. 260 - 278

Published: June 8, 2021

Parkinson disease is characterized by dopaminergic cell loss in the substantia nigra of midbrain. There are various imaging markers for disease. Recent advances MRI have enabled elucidation underlying pathophysiologic changes nigral structure. This has contributed to accurate and early diagnosis improved progression monitoring. article aims review recent developments other parkinsonian syndromes, including nigrosome imaging, neuromelanin quantitative iron mapping, diffusion-tensor imaging. In particular, this examines using 7-T 3-T susceptibility-weighted Finally, discusses volumetry its clinical importance related symptom manifestation. will improve understanding advancements Published under a CC BY 4.0 license.

Language: Английский

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Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson’s disease

Computer Methods and Programs in Biomedicine, Journal Year: 2020, Volume and Issue: 198, P. 105793 - 105793

Published: Oct. 15, 2020

Background and objectives: Qualitative quantitative analyses of Magnetic Resonance Imaging (MRI) scans are carried out to study understand Parkinson's Disease, the second most common neurodegenerative disorder in people at their 60's. Some based on application voxel-based morphometry (VBM) magnetic resonance images determine regions interest, within gray matter, where there is a loss nerve cells that generate dopamine. This dopamine indicative disease. The purpose this research introduction new method classify 3-D an individual, as assisting tool for diagnosis disease by using largest MRI dataset (Parkinson's Progression Markers Initiative) from population patients with control individuals. A contribution separate studies conducted men women since gender plays significant role Neurobiology, which demonstrated fact more prone than are. Methods: Previous classification, VBM detect features extracted first- second-order statistics methods. Furthermore, number considerably reduced feature selection techniques. Seven classifiers used we conducting experiments women. Results: best detection performance achieved 99.01% accuracy, 99.35% sensitivity, 100% specificity, precision. 96.97% 96.15% 97.22% During classification images, corresponding computational complexity few selected. Conclusions: proposed provides high disease, While previous works have focused analysis striatum region brain (the nuclear complex basal ganglia), approach over whole looking decreases tissue thickness, consequence finding other interest such cortex.

Language: Английский

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