Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Shukla, Amar; Tiwari, Rajeev; Tiwari, Shamik

doi:10.1002/ima.22967

Cited by 9 publications

(3 citation statements)

References 49 publications

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“… Shukla et al (2024b) analyzed a subcortical structure in AD using ensemble learning approaches. The combination of MRI and PET modalities is shown in this work, which could boost result visualization.…”

Section: Related Workmentioning

confidence: 99%

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Ganesan,

Ramesh,

Falkowski-Gilski

et al. 2024

Front. Physiol.

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Introduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations.Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification.Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

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Section: Related Workmentioning

confidence: 99%

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Ganesan,

Ramesh,

Falkowski-Gilski

et al. 2024

Front. Physiol.

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“…Consequently, this approach created distinct datasets consisting of patch sizes ranging from 32 to 88. In [30], the authors employed a structural biomarker known as structural magnetic resonance imaging to examine the alterations in neurostructure within various brain regions among individuals with AD, mild cognitive impairment (MCI), and those with normal cognitive function. The study employed conventional machine learning and ensemble learning models to identify AD and its various subtypes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease

Khalil,

Khan Mamun,

Sherif

et al. 2023

Sensors

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Alzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer’s disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices’ raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.

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“…Promisingly, a growing number of brain MRI techniques can serve as a useful non-invasive biomarker to gauge region-specific neurodegenerative processes, including Alzheimer's diseaserelated early changes (Kress et al, 2023;Shukla et al, 2023;Xie et al, 2023). Recent studies suggested that a variety of brain structures (e.g., fornix, cingulum, entorhinal and parahippocampal cortices, posterior cingulate, and precuneus) may exhibit changes early in the Alzheimer's disease process (Berron et al, 2020;Echavarri et al, 2011;Lacalle-Aurioles and Iturria-Medina, 2023).…”

Section: Introduction (818 Words)mentioning

confidence: 99%

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

Lee

Kim

Prado-Rico

et al. 2023

Preprint

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Introduction: Welding fumes contain a mixture of metals and are an epitype for environmental metal-related neurotoxicity. Past studies of welders focused largely on basal ganglia regions and their association with parkinsonism. This study evaluated medial temporal lobe (MTL) structures that play a key role in Alzheimer's disease-like pathology. Methods: Exposure history and whole blood metal levels were obtained from subjects with/without a history of welding (42 welders; 31 controls). MTL regions of interest (ROI) (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume and cortical thickness) and diffusion tensor imaging [mean (MD), axial (AD), radial diffusivity (RD) and fractional anisotropy (FA)] metrics. Cognition was evaluated using standard neuropsychological tests. Results: Welders had higher blood levels of Cu, Fe, K, Mn, Pb, Se, and Zn (p's<0.026) than controls. Welders had higher MD, AD, and RD in all MTL ROIs (p's<0.040) and lower FA in the entorhinal and parahippocampal cortices (p's<0.033) without significant morphologic differences. Welders also exhibited lower performance on processing/psychomotor speed, executive, and visuospatial function domains (p's<0.046). Greater welding years predicted lower parahippocampal FA (p=0.011), where greater short-term welding intensity (E90) predicted worse Symbol coding digit scores (p=0.019). Blood Mn and Cu levels demonstrated robust relationships with entorhinal diffusivity (p's<0.009) and story recall performance (p=0.003), respectively, throughout correlation, semi-parametric regression, and Bayesian kernel machine regression analyses. Discussion: Welding fumes and related metal exposures are associated with MTL features of early Alzheimer's disease. Given the ubiquitous nature of metal exposure, future studies are warranted and may have public health implications.

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Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Cited by 9 publications

References 49 publications

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

Detection of Alzheimer’s disease using Otsu thresholding with tunicate swarm algorithm and deep belief network

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer’s Disease

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

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