A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

By Saima Rathore, Mohamad Habes, Muhammad Aksam Iftikhar, Amanda Shacklett, Christos Davatzikos. NeuroImage, 2017

597 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7452ec5671671d06aa9607c38e757571b5e3d148

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Dinggang Shen
2. Olivier Colliot
3. Mingxia Liu

In Psychiatry and Mental health

1. Jun Zhang
2. Andrew J. Saykin
3. Dinggang Shen
4. Olivier Colliot
5. Heung-Il Suk
6. Seong-Whan Lee
7. Yanqing Zhang
8. Jyoti Islam

In Radiology, Nuclear Medicine and Imaging

1. Jorge Samper-González
2. Elina Thibeau-Sutre
3. Dinggang Shen
4. Antonio Cerasa
5. Alessia Sarica
6. Junhao Wen
7. Alexandre Routier
8. Aldo Quattrone

Citations in the corpus

• Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review (2017, background): “Indeed, as recently stated by Rathore et al. (2017), no single neuroimaging modality is enough to reach optimal accuracy for automatic AD prediction, but only through the combination of different methodologies, the classification task could be effectively translated into the clinical realm.”
• Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI (2018, methodology): “Existing sMRI-based CAD methods usually contain three fundamental components [4], i.”
• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, methodology): “SVM has been used in many AD classification studies and obtained competitive balanced accuracies ​(Falahati et al., 2014; Haller et al., 2011; Rathore et al., 2017)​.”
• Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data (2019, methodology): “Well-known pattern analysis methods, such as linear discriminant analysis (LDA), linear program boosting method (LPBM), logistic regression (LR), support vector machine (SVM), and support vector machinerecursive feature elimination (SVM-RFE), have been used and hold promise for early detection of AD”
• Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease (2025, unknown): “The process involves neuroimage pre-processing, segmentation, and feature extraction, followed by classification and prediction that support early diagnosis of Alzheimer’s dementia [101–103].”