Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model

Kaplan, Ela; Doğan, Şengül; Tuncer, Türker; Bayğın, Mehmet; Altunışık, Erman

doi:10.1016/j.compbiomed.2021.104828

Cited by 31 publications

(11 citation statements)

References 52 publications

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“…The existing methods were trained on the binary and multi-class classification on same dataset utilized in this article. DeepCurvMRI is compared to models such as VGG-16 [60], AlexNet [64], DEMENT [65], SVM [61], HTLML [63], and Feed-forward LPQNet [62]. LPQNET achieved slightly higher accuracy than DeepCurvMRI for the binary classification of AD.…”

Section: A Experimentation Results Of the Proposed Deepcurvmri Modelmentioning

confidence: 99%

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

2023

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Alzheimer's Disease (AD) is the most common form of dementia. It usually manifests through progressive loss of cognitive function and memory, subsequently impairing the person's ability to live without assistance and causing a tremendous impact on the affected individuals and society. Currently, AD diagnosis relies on cognitive tests, blood tests, behavior assessments, brain imaging, and medical history analysis. However, these procedures are subjective and inconsistent, making an accurate prediction for the early stages of AD difficult. This paper introduces a curvelet transform (CT) based-convolutional neural network (CNN) (DeepCurvMRI) model for improving the accuracy of early-stage AD disease detection using from Magnetic resonance imaging (MRI) images. The MRI images were first pre-processed using CT, and then a CNN model was trained using the new image representation. In this study, we used Alzheimer's MRI images dataset hosted on the Kaggle platform to train DeepCurvMRI for multi and binary classification tasks. DeepCurvMRI achieved an accuracy, sensitivity, specificity, and F1 score of 98.62% ± 0.10%, 99.05% ± 0.10%, 98.50% ± 0.03%, and 99.21 ± 0.08, respectively, using the leave-one-group-out (LOGO) cross-validation approach in multi-classification task. The highest accuracy obtained in binary classification is 98.71% ± 0.05%. In addition to LOGO, DeepCurvMRI was tested using randomly stratified 10-fold and 5-fold cross validation. These encouraging results are superior to the ones reported in related methods, showcasing the potentiality of DeepCurvMRI in capturing the key anatomical changes in MRI images that can be differentiated between various staged of Alzheimer's disease classes.INDEX TERMS Alzheimer's disease, curvelet transform, deep learning, CNN, MRI images.

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Section: A Experimentation Results Of the Proposed Deepcurvmri Modelmentioning

confidence: 99%

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

2023

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“…According to the OPLS-DA models, we initially screened eight bile acids that could distinguish three kinds of bile. However, when establishing a machine learning model, not every feature contributes significantly to the predicted target value ( Kaplan et al, 2021 ). Removing low contributing variables and using key feature markers to construct machine learning models can improve the generalization ability of the models.…”

Section: Resultsmentioning

confidence: 99%

An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile

Liang²,

Su³

et al. 2022

Front. Chem.

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Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioactive constituents of bile and contain a variety of isomeric constituents. Although the components are structurally similar, they exhibit different pharmacological activities. Identifying the characteristics of each animal bile is particularly important for processing and reuse. It is necessary to establish an accurate analysis method to distinguish different types of animal bile. We evaluated the biological activity of key feature markers from various animal bile samples. In this study, a strategy combining metabolomics and machine learning was used to compare the bile of three different animals, and four key markers were screened. Quantitative analysis of the key markers showed that the levels of Glycochenodeoxycholic acid (GCDCA) and Taurodeoxycholic acid (TDCA) were highest in pig bile; Glycocholic acid (GCA) and Cholic acid (CA) were the most abundant in bovine and sheep bile, respectively. In addition, four key feature markers significantly inhibited the production of NO in LPS-stimulated RAW264.7 macrophage cells. These findings will contribute to the targeted development of bile in various animals and provide a basis for its rational application.

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“…The LPQNet was designed to have high accuracy and low computational complexity. The model was tested on a private AD dataset and the Kaggle dataset, achieving 99.62% accuracy on the Kaggle dataset using four classes 14 . In another study, Kaplan et al used vision transformers and generated 16 exemplars.…”

Section: Labeling Dementia Stagesmentioning

confidence: 99%

Histogram-based features track Alzheimer's progression in brain MRI

Pasnoori,

Flores-Garcia,

Barkana

2024

Sci Rep

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Alzheimer's disease is a form of general dementia marked by amyloid plaques, neurofibrillary tangles, and neuron degeneration. The disease has no cure, and early detection is critical in improving patient outcomes. Magnetic resonance imaging (MRI) is important in measuring neurodegeneration during the disease. Computer-aided image processing tools have been used to aid medical professionals in ascertaining a diagnosis of Alzheimer's in its early stages. As characteristics of non and very-mild dementia stages overlap, tracking the progression is challenging. Our work developed an adaptive multi-thresholding algorithm based on the morphology of the smoothed histogram to define features identifying neurodegeneration and track its progression as non, very mild, mild, and moderate. Gray and white matter volume, statistical moments, multi-thresholds, shrinkage, gray-to-white matter ratio, and three distance and angle values are mathematically derived. Decision tree, discriminant analysis, Naïve Bayes, SVM, KNN, ensemble, and neural network classifiers are designed to evaluate the proposed methodology with the performance metrics accuracy, recall, specificity, precision, F1 score, Matthew’s correlation coefficient, and Kappa values. Experimental results showed that the proposed features successfully label the neurodegeneration stages.

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Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model

Cited by 31 publications

References 52 publications

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

DeepCurvMRI: Deep Convolutional Curvelet Transform-Based MRI Approach for Early Detection of Alzheimer’s Disease

An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile

Histogram-based features track Alzheimer's progression in brain MRI

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