Multi-class Classification of Alzheimer's Disease using 3DCNN Features and Multilayer Perceptron

Raju, Manu; Gopi, Varun P.; Anitha, V.

doi:10.1109/wispnet51692.2021.9419393

Cited by 22 publications

(7 citation statements)

References 25 publications

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“…Raju et al [ 10 ] proposed a multi-class classification approach for AD using 3DCNN features and a Multilayer Perceptron classifier, achieving an accuracy of 96.66 %. The authors implemented a patch-based method, 3D-CNN for feature extraction, and a multilayer neural network for classification.…”

Section: Related Workmentioning

confidence: 99%

“…AD For fMRI Dataset: 99.95 %. For PET Dataset: 73.46 % [ 9 ] MRI CNN MCIc vs. MCInc 94 % [ 10 ] MRI 3D-CNN MCI vs. CN vs. AD 96.66 % [ 11 ] MRI MLP MCI vs. CN vs.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, unlike Mukhtar and Farhan [ 9 ], who focused on predicting the conversion from Mild Cognitive Impairment (MCI) to AD using diverse features, our model contributes by providing accurate classification into three distinct classes: cognitively normal (CN), MCI, and AD. Furthermore, in contrast to the patch-based method implemented by Raju et al [ 10 ], our study employs advanced image preprocessing techniques, ensuring a streamlined and efficient process for early AD detection. The proposed solution stands out by achieving high accuracy rates and incorporating a user-friendly website for practical clinical applications, distinguishing it as a comprehensive and impactful contribution to the field.…”

Section: Related Workmentioning

confidence: 99%

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AI-based tool for early detection of Alzheimer's disease

Ul Rehman,

Tarek,

Magdy

et al. 2024

Heliyon

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Section: Related Workmentioning

confidence: 99%

“…AD For fMRI Dataset: 99.95 %. For PET Dataset: 73.46 % [ 9 ] MRI CNN MCIc vs. MCInc 94 % [ 10 ] MRI 3D-CNN MCI vs. CN vs. AD 96.66 % [ 11 ] MRI MLP MCI vs. CN vs.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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AI-based tool for early detection of Alzheimer's disease

Ul Rehman,

Tarek,

Magdy

et al. 2024

Heliyon

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“…MLC is a machine learning approach that allows instances to be categorized into multiple, potentially overlapping classes simultaneously, expanding beyond the scope of traditional single-label classification [57], [58], [59], [60], [61]. Commonly used in fields like text categorization, image tagging, and bioinformatics, MLC determines the presence or absence of each label for a given instance.…”

Section: B Deep Learning Models For Rfeh Component Designmentioning

confidence: 99%

Deep Learning Design Framework: High-Precision Front-End RFEH for Food Monitoring Applications

Nando,

Chung

2024

IEEE Trans. Antennas Propagat.

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This paper introduces an innovative deep learning design framework for front-end radio frequency energy harvesting (RFEH), employing stacking ensemble learning (SEL). The framework integrates multi-output regression (MOR), multilabel classification (MLC), and multivariate time series (MTS) in sequence to achieve accurate prediction (R 2 > 0.90) and classification (> 0.95) at each stage. By transforming frequency to datetime and consolidating output parameters into consecutive time intervals, our approach intelligently combines receiving antennas, impedance matching networks (IMNs), and voltage multipliers (VMs) through MOR and MLC, while MTS guides the determination of geometrical parameters and values. To validate our method, we developed an efficient food sensor tag embedded with an RFEH circuit operating at 915 MHz. The tag, featuring antennas designed by MTS (including an inset-fed rectangular microstrip patch antenna [RMPA], a folded collinear antenna [FCA], and a quasi-Yagi antenna [QYA]), achieves a power conversion efficiency (PCE) of 55.17% with an input power of 6.25 dBm, demonstrating stable sensing capabilities. This integrated deep learning framework holds promise for tailored front-end RFEH design in food quality monitoring application, with significant implications across industries.Index Terms-Antenna design, food monitoring application, front-end RF energy harvesting (RFEH), multi-output regression (MOR), multilabel classification (MLC), multivariate time series (MTS), and stacking ensemble learning (SEL).

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“…Understanding each stage is crucial for the accurate diagnosis of the disease at each level. Most previous studies focused only on binary classification tasks, and there was a deficiency in multiclass classification tasks [78]. Hence, we targeted both binary and multiclass tasks.…”

Section: B Ablation Studymentioning

confidence: 99%

Automatic Early Diagnosis of Alzheimer’s Disease Using 3D Deep Ensemble Approach

2022

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Alzheimer's disease (AD) is considered the 6 th leading cause of death worldwide. Early diagnosis of AD is not an easy task, and no preventive cures have been discovered yet. Having an accurate computer-aided system for the early detection of AD is important to help patients with AD. This study proposes a new approach for classifying disease stages. First, we worked on the MRI images and split them into an appropriate format to avoid data leakage. Subsequently, a simple and fast registration-free preprocessing pipeline was applied to the dataset. Numerous experiments were conducted to analyze the performances of different 3D classification architectures. Finally, an ensemble learning approach is applied to the top-performing models. The outstanding performance of the proposed method was demonstrated using augmentation of the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our proposed ensemble approach outperforms studies in literature for distinguishing between people with AD and mild cognitive impairment (MCI), and MCI and cognitive normal (CN) with an AUC score of 91.28% and 88.42%, respectively. We also targeted the multiclass task, which was marginalized in previous work, by differentiating between the three stages of the disease.

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Multi-class Classification of Alzheimer's Disease using 3DCNN Features and Multilayer Perceptron

Cited by 22 publications

References 25 publications

AI-based tool for early detection of Alzheimer's disease

AI-based tool for early detection of Alzheimer's disease

Deep Learning Design Framework: High-Precision Front-End RFEH for Food Monitoring Applications

Automatic Early Diagnosis of Alzheimer’s Disease Using 3D Deep Ensemble Approach

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