andrealoddo / AD_classification

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Deep learning based pipeline for Alzheimer's disease diagnosis: a comparative study

Models and Features inherent the manuscript "Deep learning based pipeline for Alzheimer's disease diagnosis: a comparative study"

Here, you can find the models and the features used in the manuscript indicated above.

Authors: Andrea Loddo and Sara Buttau (University of Cagliari). Releasers and maintainers: Andrea Loddo (University of Cagliari).

NOTE: please cite one of the following pubblication in case of using these images in your own work:

TDB

OASIS

is a project aimed at freely distributing brain MRI data, including two comprehensive data sets. The sagittal data set includes MRI data of 416 subjects (young, middle-aged, non-demented, and demented older adults) aged between 18 to 96. The longitudinal data set includes MRI data of 150 subjects (non-demented and demented older adults) aged between 60 to 96. It has been described in the following article: https://direct.mit.edu/jocn/article/19/9/1498/4427/Open-Access-Series-of-Imaging-Studies-OASIS-Cross. More information can be found @ https://www.oasis-brains.org/

Alzheimer-MRI

Alzheimer-MRI data set consists of a total of 5,121 axial images. Each image is labelled with the corresponding level of dementia: no dementia, very mild dementia, mild dementia and moderate dementia. The age of the patients is unknown and no other data about them are provided. The data set includes 2,560 healthy subjects and 2,561 subjects with dementia (1,792 with very mild dementia, 717 with mild dementia, 52 with moderate dementia). Examples are shown in Fig.~\ref{fig:kaggle}. More information can be found @ https://www.kaggle.com/legendahmed/alzheimermridataset/metadata

ADNI

ADNI is a project underway since 2004 to follow the progress of AD through its biomarkers in order to diagnose the disease in its early stages. Currently, ADNI is divided into three phases: ADNI1, ADNI GO/2 and ADNI3. ADNI registers participants aged 55-90 years among 57 sites in the United States and Canada. More information can be found @ http://adni.loni.usc.edu}

LICENSE

MIT License

Copyright (c) 2021 Andrea Loddo, Sara Buttau, Cecilia Di Ruberto

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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