Early Detection of Alzheimer’s Disease From Cortical and Hippocampal Local Field Potentials Using an Ensembled Machine Learning Model

Fabietti, Marcos; Mahmud, Mufti; Lotfi, Ahmad; Leparulo, Alessandro; Fontana, Roberto; Vassanelli, Stefano; Fasolato, Cristina

doi:10.1109/tnsre.2023.3288835

Cited by 11 publications

(2 citation statements)

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“…In the result analysis, the proposed model achieved 97.57% accuracy and a sensitivity of 97.60%. M. Fabietti et al [24] introduced the Ensembled ML model with explainability (EXML). The author aimed to locate minute patterns in cortical and hippocampus Local Field Potential (LFP) signals that may predict AD in its early stages.…”

Section: A Analysis Of Admentioning

confidence: 99%

“…Meanwhile, ADNI [1], [45], [47], and the Open Access Series of Imaging Studies (OASIS) [36], [37] stand out as significant contributors to advancing research in this domain. Structural MRI [2], [18], [27], [32], [33], [36], [39] OASIS MRI MRI images with a focus on brain tumor detection Structural MRI [5], [8], [14], [15], [17], [19], [21], [22], [24], [25], [36], [37], [38], [42], [43] MRI ADNI Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset Functional and Structural MRI, PET [1], [4], [4], [7], [16], [23], [28], [29], [30], [40], [41], [44], [45] ADNI GARD Gwangju Alzheimer's and Related Dementia (GARD) dataset…”

Section: Figure 5 Ad Detection Using Various Technique Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial Network-Based Classification for Alzheimer’s Disease Using Multimodal Brain Images: A Critical Analysis

Gupta,

Kumar,

Abraham

2024

IEEE Access

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Alzheimer's disease (AD) is a progressive neurodegenerative disorder that represents a significant and growing public health challenge. This work concisely summarizes AD, encompassing its pathophysiology, risk factors, clinical manifestations, diagnosis, treatment, and ongoing research. The main goal of managing AD is to reduce symptoms while improving the lives of those impacted. This letter has conducted a systematic review to analyze the prediction of AD using the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) guidelines. The major scientific databases such as Scopus, Web of Science (WoS), and IEEE Xplorer are explored, where 2018-2023 publications are considered. The article selection process is based on keywords like "Alzheimer's disease," "Brain Images," "Deep Learning (DL)," etc. After rigorous analysis, 946 articles were extracted, and 42 were identified for final consideration. Further, several investigations based on the previous work are discussed along with its Proposed Solutions (PS). Finally, a case study on AD detection using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and AD Detection Network (ADD-NET) implementation is presented.

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Section: A Analysis Of Admentioning

confidence: 99%