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- W3034151505 endingPage "3243" @default.
- W3034151505 startingPage "3243" @default.
- W3034151505 abstract "Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD." @default.
- W3034151505 created "2020-06-12" @default.
- W3034151505 creator A5030344955 @default.
- W3034151505 creator A5032242558 @default.
- W3034151505 creator A5051083488 @default.
- W3034151505 date "2020-06-07" @default.
- W3034151505 modified "2023-10-09" @default.
- W3034151505 title "MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey" @default.
- W3034151505 cites W1568194840 @default.
- W3034151505 cites W1884191083 @default.
- W3034151505 cites W1901624583 @default.
- W3034151505 cites W1963847477 @default.
- W3034151505 cites W1970300450 @default.
- W3034151505 cites W1976076281 @default.
- W3034151505 cites W1976154578 @default.
- W3034151505 cites W1981789870 @default.
- W3034151505 cites W1984020445 @default.
- W3034151505 cites W1987011701 @default.
- W3034151505 cites W1987869189 @default.
- W3034151505 cites W1991952617 @default.
- W3034151505 cites W1992974274 @default.
- W3034151505 cites W2001648635 @default.
- W3034151505 cites W2001773046 @default.
- W3034151505 cites W2015629132 @default.
- W3034151505 cites W2019161608 @default.
- W3034151505 cites W2028158228 @default.
- W3034151505 cites W2041398617 @default.
- W3034151505 cites W2042630303 @default.
- W3034151505 cites W2046142779 @default.
- W3034151505 cites W2049571912 @default.
- W3034151505 cites W2050083500 @default.
- W3034151505 cites W2054183703 @default.
- W3034151505 cites W2057332541 @default.
- W3034151505 cites W2066839705 @default.
- W3034151505 cites W2070181269 @default.
- W3034151505 cites W2073272031 @default.
- W3034151505 cites W2078524519 @default.
- W3034151505 cites W2078998718 @default.
- W3034151505 cites W2082526668 @default.
- W3034151505 cites W2084258254 @default.
- W3034151505 cites W2086552679 @default.
- W3034151505 cites W2087159877 @default.
- W3034151505 cites W2093092963 @default.
- W3034151505 cites W2097775060 @default.
- W3034151505 cites W2098056602 @default.
- W3034151505 cites W2098765040 @default.
- W3034151505 cites W2101282194 @default.
- W3034151505 cites W2101608218 @default.
- W3034151505 cites W2102099319 @default.
- W3034151505 cites W2102508963 @default.
- W3034151505 cites W2104598584 @default.
- W3034151505 cites W2106904753 @default.
- W3034151505 cites W2106931873 @default.
- W3034151505 cites W2111913931 @default.
- W3034151505 cites W2112398095 @default.
- W3034151505 cites W2113708991 @default.
- W3034151505 cites W2115424422 @default.
- W3034151505 cites W2117530999 @default.
- W3034151505 cites W2117539524 @default.
- W3034151505 cites W2118421222 @default.
- W3034151505 cites W2119848633 @default.
- W3034151505 cites W2122320288 @default.
- W3034151505 cites W2122632052 @default.
- W3034151505 cites W2126598020 @default.
- W3034151505 cites W2130371234 @default.
- W3034151505 cites W2131437398 @default.
- W3034151505 cites W2136579519 @default.
- W3034151505 cites W2137732937 @default.
- W3034151505 cites W2138878035 @default.
- W3034151505 cites W2142592339 @default.
- W3034151505 cites W2147800946 @default.
- W3034151505 cites W2150534249 @default.
- W3034151505 cites W2153171432 @default.
- W3034151505 cites W2154123014 @default.
- W3034151505 cites W2158335018 @default.
- W3034151505 cites W2160034813 @default.
- W3034151505 cites W2162333503 @default.
- W3034151505 cites W2163112810 @default.
- W3034151505 cites W2168283959 @default.
- W3034151505 cites W2171831801 @default.
- W3034151505 cites W2284198383 @default.
- W3034151505 cites W2291160690 @default.
- W3034151505 cites W2292862470 @default.
- W3034151505 cites W2301358467 @default.
- W3034151505 cites W2310992461 @default.
- W3034151505 cites W2341106171 @default.
- W3034151505 cites W2342591535 @default.
- W3034151505 cites W2343172899 @default.
- W3034151505 cites W2401520370 @default.
- W3034151505 cites W2413582275 @default.
- W3034151505 cites W2460515352 @default.
- W3034151505 cites W2493683088 @default.
- W3034151505 cites W2507931172 @default.
- W3034151505 cites W2551562422 @default.
- W3034151505 cites W2567599812 @default.
- W3034151505 cites W2574038793 @default.
- W3034151505 cites W2575552683 @default.
- W3034151505 cites W2589647984 @default.