3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI

Yağış, Ekin; Citi, Luca; Diciotti, Stefano; Marzi, Chiara; Atnafu, Selamawet Workalemahu; García, Narciso

doi:10.1109/cbms49503.2020.00020

Cited by 33 publications

(11 citation statements)

References 37 publications

(37 reference statements)

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“…The architecture is built using Keras with TensorFlow backend by Yagis, et al [13]. In Data preprocessing: here the data are modified into a standardized structure.…”

Section: Existing Methodologiesmentioning

confidence: 99%

Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

Kamath¹,

Fathima²,

Monica³

et al. 2021

IJAI

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Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.

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“…The architecture is built using Keras with TensorFlow backend by Yagis, et al [13]. In Data preprocessing: here the data are modified into a standardized structure.…”

Section: Existing Methodologiesmentioning

confidence: 99%

Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

Kamath¹,

Fathima²,

Monica³

et al. 2021

IJAI

View full text Add to dashboard Cite

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“…Finally, when looking at the 10 additional papers acquired from the IEEE Digital Library, four used three-dimensional deep learning [ 21 , 22 , 23 , 24 ]. We believe that, as mentioned by [ 21 ], three-dimensional architectures are being initiated.…”

Section: Previous Workmentioning

confidence: 99%

“…We consider that this kind of architecture is growing in use. We also continued to find research efforts that only report high accuracy, but not other metrics [ 24 , 25 , 26 , 27 , 28 ]. Thus, the paradigm of obtaining the highest possible accuracy in classification remains.…”

Section: Previous Workmentioning

confidence: 99%

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Solano-Rojas

Ricardo

2021

Sensors

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Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86¯ mean accuracy, 0.86¯ mean sensitivity (micro-average), 0.86¯ mean specificity (micro-average), and 0.91¯ area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.

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“…This study compared 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. Various studies used 3D features extracted from human brain signals as inputs to their designed 3D CNN architectures [31]- [34]. Generally, two types of 3D features were used in current studies regarding human brain signals.…”

Section: Introductionmentioning

confidence: 99%

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection

Zhang

Yang

Fan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multidimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

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3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI

Cited by 33 publications

References 37 publications

Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection

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