Edge detection for diagnosis early Alzheimer's disease by using Weibull distribution

Al-Jibory, Wafaa Kamel; El-Zaart, Ali

doi:10.1109/icm.2013.6735024

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…To begin, the effects of each slicing method on the findings are discussed, as seen in Table 6 . The slicing method is then determined by consulting the literature [ 16 , 17 ], which is summarized as follows: The MRI scans of each person are cut axially, sagittal, and coronal. Then, 32 MRI slices in the center are chosen, including 11 axial, 11 sagittal, and 10 coronal slices In this step, you choose 32 axial slices for each person's MRI image slice that have the most information entropy, which is how much information there is in each slice …”

Section: Methodsmentioning

confidence: 99%

“…Among other things, one way to do this is to sort by location, with the slices closest to the brain's center being kept. If not, they are thrown away [ 16 , 17 ]. A different way is to sort by picture entropy and keep the slices with the most entropy.…”

Section: Dimension Matching Problemmentioning

confidence: 99%

“…While the network files are publicly available, the pretraining weights are not, extending the training time and imposing further development constraints on the ability to grow applications. AlexNet [ 16 ] and VGG16 [ 17 ] have both been very good at diagnosing AD and so have two-dimensional transfer networks, like mobile networks. It starts with AlexNet and VGG16, then slices the MRI image, and chooses different slices depending on where they are and how much information they have.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Gupta

Saravanan²,

Choudhury³

et al. 2022

Computational and Mathematical Methods in Medicine

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Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

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Section: Methodsmentioning

confidence: 99%

Section: Dimension Matching Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Gupta

Saravanan²,

Choudhury³

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Nowadays, many organizations release their clients' datasets to third parties for training DL models that help in making decisions [91]- [93]. However, providing dataset to a third-party may violate users' privacy and breach privacy laws [94], [95].…”

Section: Privacy-preserving Datasetsmentioning

confidence: 99%

Differential Privacy for Deep and Federated Learning: A Survey

2022

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Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information of users may be disclosed during data collection, or during training, or even after releasing the trained learning model. Differential privacy (DP) is one of the main approaches proven to ensure strong privacy protection in data analysis. DP protects the users' privacy by adding noise to the original dataset or the learning parameters. Thus, an attacker could not retrieve the sensitive information of an individual involved in the training dataset. In this survey paper, we analyze and present the main ideas based on DP to guarantee users' privacy in deep and federated learning. In addition, we illustrate all types of probability distributions that satisfy the DP mechanism, with their properties and use cases. Furthermore, we bridge the gap in the literature by providing a comprehensive overview of the different variants of DP, highlighting their advantages and limitations. Our study reveals the gap between theory and application, accuracy, and robustness of DP. Finally, we provide several open problems and future research directions.

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“…Being applied to images using Weibull distribution, the structured masks yield good results for the diagnosis of the early Alzheimer's disease [1]. The paper [2] explores the relationship between the visible content and the real image statistics sampled by the integrated Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

El-Sayed¹,

Riad²,

Elsafty³

et al. 2017

JCC

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The purpose of this article offers different algorithms of Weibull Geometric (WG) distribution estimation depending on the progressive Type II censoring samples plan, spatially the joint confidence intervals for the parameters. The approximate joint confidence intervals for the parameters, the approximate confidence regions and percentile bootstrap intervals of confidence are discussed, and several Markov chain Monte Carlo (MCMC) techniques are also presented. The parts of mean square error (MSEs) and credible intervals lengths, the estimators of Bayes depend on non-informative implement more effective than the maximum likelihood estimates (MLEs) and bootstrap. Comparing the models, the MSEs, average confidence interval lengths of the MLEs, and Bayes estimators for parameters are less significant for censored models.

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Edge detection for diagnosis early Alzheimer's disease by using Weibull distribution

Cited by 7 publications

References 8 publications

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer’s Disease-Based Neurodegenerative Disorders

Differential Privacy for Deep and Federated Learning: A Survey

Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

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