JCSE, vol. 16, no. 1, pp.1-13, 2022

DOI: http://dx.doi.org/10.5626/JCSE.2022.16.1.1

Deep and Statistical-Based Methods for Alzheimer's Disease Detection: A Survey

Marwa Zaabi, Nadia Smaoui, Walid Hariri, and Houda Derbel
CEM Laboratory, ENIG, Gabes University, Gabes, Tunisia CEM Laboratory, ENIS, Sfax University, Sfax, Tunisia LABGED Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria CEM Laboratory, ENIS, Sfax University, Sfax, Tunisia

Abstract: Detection of Alzheimer's disease (AD) is one of the most potent and daunting activities in the processing of medical imagery. The survey of recent AD detection techniques in the last 10 years is described in this paper. The AD detection process involves various steps, namely preprocessing, feature extraction, feature selection, dimensionality reduction, segmentation and classification. In this study, we reviewed the latest findings and possible patterns as well as their main contributions. Different types of AD detection techniques are also discussed. Based on the applied algorithms and methods, and the evaluated databases (e.g., ADNI and OASIS), the performances of the most relevant AD detection techniques are compared and discussed.

Keyword: Alzheimer's disease; Statistical methods; Deep learning methods; Segmentation methods

Full Paper:   227 Downloads, 1347 View