Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI

Buvaneswari, P. R.; Gayathri, R.

doi:10.1007/s00521-021-06436-2

Cited by 12 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The large number of samples (6,028) used it also relevant when compared with similar investigations [10]. The results show that our model has outperformed other modern experiments [5][6][7]14,24,25]. A more detailed comparison with some of the most promising investigations conducted to date is made in Table 8.…”

mentioning

confidence: 83%

“…This research will thoroughly analyze the full content of the MRI images and contrast them again the afore-mentioned experiments, aiming to predict up to six different stages of dementia. The objective is to use the nine best slices proposed in [10] and simultaneously apply two different approaches as suggested in [13,14]: two-dimensional multiresolution analysis (2-D MRA) in L 2 (R 2 ), aligned with SVM and convolutional neural network (CNN) [15,16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

Prado¹,

Rojas

2021

BioMedInformatics

View full text Add to dashboard Cite

According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early diagnosis is key to promoting early and optimal management. However, the early stage of dementia is often overlooked and patients are typically diagnosed when the disease progresses to a more advanced stage. The objective of this contribution is to predict Alzheimer’s early stages, not only dementia itself. To carry out this objective, different types of SVM and CNN machine learning classifiers will be used, as well as two different feature selection algorithms: PCA and mRMR. The different experiments and their performance are compared when classifying patients from MRI images. The newness of the experiments conducted in this research includes the wide range of stages that we aim to predict, the processing of all the available information simultaneously and the Segmentation routine implemented in SPM12 for preprocessing. We will make use of multiple slices and consider different parts of the brain to give a more accurate response. Overall, excellent results have been obtained, reaching a maximum F1 score of 0.9979 from the SVM and PCA classifier.

show abstract

mentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

Prado¹,

Rojas

2021

BioMedInformatics

View full text Add to dashboard Cite

show abstract

“…Principal component analysis (PCA) reduced the feature dimension of the fMRI resting-state images. The support vector regression (SVR) method classifies AD and MCI datasets with an accuracy of 98.53% [9]. The XG-Boost classifier on the ADNI dataset achieved an accuracy of 85.92% [10].…”

Section: Introductionmentioning

confidence: 99%

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Alharbi¹,

Ziyad²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Aging is a natural process that leads to debility, disease, and dependency. Alzheimer's disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient's disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.

show abstract

“…The accuracy of MCI vs. NC was 76.4%, which was an improvement of 4.4–5% compared to using single modality data. Buvaneswari and Gayathri (2021) combined the features extracted from DTI and fMRI into a multikernel SVM for AD classification, and the accuracy of AD vs. NC was 98.4%; however, when the two modalities were used alone for classification, the highest achieved accuracy was only 90.9%. The above research further verifies that in the classification of AD, compared with single-modal data, the use of multimodal data can obtain richer and more valuable features, and the classifier can obtain higher classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multimodal neuroimage data fusion based on multikernel learning in personalized medicine

et al. 2022

View full text Add to dashboard Cite

Neuroimaging has been widely used as a diagnostic technique for brain diseases. With the development of artificial intelligence, neuroimaging analysis using intelligent algorithms can capture more image feature patterns than artificial experience-based diagnosis. However, using only single neuroimaging techniques, e.g., magnetic resonance imaging, may omit some significant patterns that may have high relevance to the clinical target. Therefore, so far, combining different types of neuroimaging techniques that provide multimodal data for joint diagnosis has received extensive attention and research in the area of personalized medicine. In this study, based on the regularized label relaxation linear regression model, we propose a multikernel version for multimodal data fusion. The proposed method inherits the merits of the regularized label relaxation linear regression model and also has its own superiority. It can explore complementary patterns across different modal data and pay more attention to the modal data that have more significant patterns. In the experimental study, the proposed method is evaluated in the scenario of Alzheimer’s disease diagnosis. The promising performance indicates that the performance of multimodality fusion via multikernel learning is better than that of single modality. Moreover, the decreased square difference between training and testing performance indicates that overfitting is reduced and hence the generalization ability is improved.

show abstract

Detection and Classification of Alzheimer’s disease from cognitive impairment with resting-state fMRI

Cited by 12 publications

References 32 publications

Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease

Multimodal neuroimage data fusion based on multikernel learning in personalized medicine

Contact Info

Product

Resources

About