A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer’s disease

Kamathe, Rupali; Joshi, Kalyani R.

doi:10.1016/j.bspc.2017.09.005

Cited by 33 publications

(5 citation statements)

References 14 publications

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“…Image modality plays a vital role in the classification of MRI-based images. T1-weighted images are used in the case of structural MRI (sMRI) images, and only a few studies use T2-based images [ 54 ]. This is because the delineation of the ventricular surface of the brain due to atrophy is clearly visible in T1-weighted images.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Malik,

Iqbal,

et al. 2024

Diagnostics

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Alzheimer’s disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer’s disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research.

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Section: Discussionmentioning

confidence: 99%

Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Malik,

Iqbal,

et al. 2024

Diagnostics

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“…Rupali S. Kamathe (2017) has developed a novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in AD [52]. The purpose of this work was to test the usefulness of ICA with different input images generated using BEP for accurate brain tissue segmentation by validating results with different quality metrics.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Extreme Learning Machine for the Diagnosis of Alzheimer's Disease

Mishra,

Arora,

Jaiswal

et al. 2024

Preprint

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The aim of this study to analyze the performance of some of the significant methods using machine learning techniques for diagnosis the Alzheimer’s disease (AD). Deep learning methods have been widely used in the AD diagnosis but Extreme learning machine (ELM) and kernel (ELM) methods have been hardly ever used. Before the deployment of these methods for computation, we present a short review in the section on related work. A series of three different cases consisting of classification models in deep learning is used. We show the computational results of three of its methods CNN, MLP and LSTM. Data set has been taken from ADNI and has been pre-processed using PCA and 10 cross-fold validation. The dataset is divided into three cases: case1, case2 and case3. The results are evaluated using two performance measures in terms of Accuracy and Error analysis. The ranking of computation methods are measured based on its performance matrices. It is observed that the performance of the proposed study to classify subjects as infected or fit using Alzheimer’s Disease Neuroimaging Initiative (ADNI*) dataset. The three cases are shuffling of presence or absence of Principal Component Analysis (PCA), and k-fold cross-validation in our operation carried out for the diagnosis. Then, a comparative study of accuracy and error as performance measures, obtained by these methods has been performed to select the best method for prediction of AD with maximum accuracy and minimum error and it is computed that the MLP deep learning method is having maximum accuracy of 82% with least error in the case3.

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“…Independent component analysis (ICA) can separate multivariate signals into statistically uncorrelated non-Gaussian constituents. ICA has been adopted for analysis of MRI data multiple times before [12], [13], [14]. However, ICA is essentially a linear analysis method, which means that after applying ICA on images the residual statistical dependences remain [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Zhang

Wei

et al. 2019

IEEE Access

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Methods for image registration, segmentation, and visualization of magnetic resonance imaging (MRI) data are used widely to help medical doctors in supporting diagnostics. The large amount and complexity of MRI data require looking for new methods that allow for efficient processing of this data. Here, we propose using the adaptive independent subspace analysis (AISA) method to discover meaningful electroencephalogram activity in the MRI scan data. The results of AISA (image subspaces) are analyzed using image texture analysis methods to calculate first order, gray-level co-occurrence matrix, gray-level size-zone matrix, gray-level run-length matrix, and neighboring gray-tone difference matrix features. The obtained feature space is mapped to the 2D space using the t-distributed stochastic neighbor embedding method. The classification results achieved using the k-nearest neighbor classifier with 10-fold crossvalidation have achieved 94.7% of accuracy (and f-score of 0.9356) from the real autism spectrum disorder dataset. INDEX TERMS Adaptive independent subspace analysis (AISA), magnetic resonance imaging (MRI), image processing, autism spectrum disorder.

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A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer’s disease

Cited by 33 publications

References 14 publications

Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review

Deep Learning and Extreme Learning Machine for the Diagnosis of Alzheimer's Disease

Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

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