Alzheimer 's disease identification from 3D SPECT brain scans by variational analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11120%2F23%3A43924345" target="_blank" >RIV/00216208:11120/23:43924345 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22340/23:43927725 RIV/00064190:_____/23:10001179 RIV/00023001:_____/23:00083585

Výsledek na webu

<a href="https://doi.org/10.1016/j.bspc.2022.104385" target="_blank" >https://doi.org/10.1016/j.bspc.2022.104385</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2022.104385" target="_blank" >10.1016/j.bspc.2022.104385</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Alzheimer 's disease identification from 3D SPECT brain scans by variational analysis

Popis výsledku v původním jazyce

The application of a radioactive tracer and following brain Single Positron Emission Computed Tomography (SPECT) is a standard technique used in neurodegenerative disease investigation. Alzheimer&apos;s disease is the most common form of neurodegenerative disease. In this paper, a novel 3D linear classifier is developed to classify Alzheimer&apos;s disease. The classification problem is formulated as the variational task with periodic boundary conditions, which is easy to discretize and solve using Fast Fourier Transform, and therefore, the resulting learning algorithm is very fast. Thanks to linearity of the classifier, weights obtained by 3D classifier learning are easy to visualize and bring understanding the most important features. The proposed classifier exhibits accuracy, sensitivity, and specificity of at least 90%.

Název v anglickém jazyce

Alzheimer 's disease identification from 3D SPECT brain scans by variational analysis

Popis výsledku anglicky

The application of a radioactive tracer and following brain Single Positron Emission Computed Tomography (SPECT) is a standard technique used in neurodegenerative disease investigation. Alzheimer&apos;s disease is the most common form of neurodegenerative disease. In this paper, a novel 3D linear classifier is developed to classify Alzheimer&apos;s disease. The classification problem is formulated as the variational task with periodic boundary conditions, which is easy to discretize and solve using Fast Fourier Transform, and therefore, the resulting learning algorithm is very fast. Thanks to linearity of the classifier, weights obtained by 3D classifier learning are easy to visualize and bring understanding the most important features. The proposed classifier exhibits accuracy, sensitivity, and specificity of at least 90%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

80

Číslo periodika v rámci svazku

Part 2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

104385

Kód UT WoS článku

000891151000009

EID výsledku v databázi Scopus

2-s2.0-85141543428