Analyzing subcortical structures in Alzheimer's disease using ensemble learning

Shukla, Amar; Tiwari, Rajeev; Tiwari, Shamik

doi:10.1016/j.bspc.2023.105407

Cited by 4 publications

(2 citation statements)

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“… Shukla et al (2024a) has demonstrated Structural biomarker-based AD detection via ensemble learning approaches. This work used ensemble learning and standard machine learning models to detect AD and its subtypes.…”

Section: Related Workmentioning

confidence: 99%

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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%

“… Shukla et al (2023) has presented AD detection from fused PET and MRI modalities with the help of an ensemble classifier. The multimodal approach is used in this paper to extensively explore the prediction of AD and its stages.…”

Section: Related Workmentioning

confidence: 99%