Diagnosis of Alzheimer Disease Using Fast Independent Component Analysis and Otsu Multi-level Thresholding

Suresha, H.S.; Parthasarathy, Srirangapatna Sampathkumaran

doi:10.22266/ijies2018.1031.07

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Data integration of methods [6], [7] resulted in an explosion of enthusiasm for using machine learning on computers to do integrative analysis. Popular pattern analysis techniques have shown promise in early AD detection and prediction of AD progression [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Krishnan

Rao²,

Saradhi³

et al. 2023

Int. j. commun. netw. inf. secur.

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Alzheimer's disease is a kind of degenerative dementia that causes progressively worsening memory loss and other cognitive and physical impairments over time. Mini-Mental State Examinations and other screening tools are helpful for early detection, but diagnostic MRI brain analysis is required. When Alzheimer's disease (AD) is detected in its earliest stages, patients may begin protective treatments before permanent brain damage has occurred. The characteristics of the lesion sites in AD affected role, as identified by MRI, exhibit great variety and are dispersed across the image space, as demonstrated in cross-sectional imaging investigations of the disease. Optimized Adaptive Bilateral filtering using a deep learning model was suggested as part of this study's approach toward this end. Denoising the pictures with the help of the suggested adaptive bilateral filter is the first stage (ABF). The ABF improves denoising in edge, detail, and homogenous areas separately. After then, the ABF is given a weight, and the Adaptive Equilibrium Optimizer is used to determine the best possible value for that weight (AEO). LeNet, a CNN model, is then used to complete the AD organization. The first step in using the LeNet-5 network model to identify AD is to study the model's structure and parameters. The ADNI experimental dataset was used to verify the suggested technique and compare it to other models. The experimental findings prove that the suggested method can achieve a classification accuracy of 97.43%, 98.09% specificity, 97.12% sensitivity, and 89.67% Kappa index. When compared against competing algorithms, the suggested model emerges victorious.

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Section: Introductionmentioning

confidence: 99%

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Krishnan

Rao²,

Saradhi³

et al. 2023

Int. j. commun. netw. inf. secur.

View full text Add to dashboard Cite

show abstract

“…The evaluation to classify AD as in [1] [19] shows an extensive process in feature extraction techniques. Although it is possible to perform the AD classification method by segmenting brain images into white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM) [20] [21], this method has the potential of increasing the computational process and the amount of data processed for each plane in the MRI. Furthermore, the quality of the chosen features during the feature extraction process is highly dependent on the image preprocessing due to registration errors and noise.…”

Section: Introductionmentioning

confidence: 99%

Multiplane Convolutional Neural Network (Mp-CNN) for Alzheimer’s Disease Classification

2022

IJIES

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One of the objectives of medical imaging research is to develop an effective and reliable clinical support tool for the early detection of various neurological conditions in patients such as Alzheimer's Disease (AD). Classification based on three-dimensional (3D) MRI (Magnetic-Resonance-Imaging) images have shown a good performance. However, solving 3D object classification as a 3D object using classical machine learning or deep learning is computationally high. 3D images from MRI can be reconstructed into three depth directions: axial, coronal, and sagittal. However, the multi-view-based method cannot explore the content of each image slice for all planes in 3D-MRI. Our study proposes a multiplane method by using three two-dimensional (2D) CNNs to capture discriminatory information on all available planes. Our method is a new fusion strategy to deal with the disadvantages of shape-based multi-view techniques. Our framework selects three slices: the three largest 3-planes to represent whole 3D-MRI objects as multi-2DCNN inputs. Extensive experiments have shown that the proposed method can outperform the 2DCNN method, which uses only one plane. More importantly, by taking full advantage of 2DCNN, we offer a new method for identifying 3D objects that is both easy and efficient. We called this new architecture Multiplane Convolutional Neural Network (Mp-CNN) since it used multiple inputs in its design. We evaluated the proposed method using T1-weighted structural MRI data consisting of 500 AD, 500 Mild Cognitive Impairment (MCI), and 500 Normal Cognition (NC) subjects collected from the MRI database of Alzheimer's Disease Neuroimaging Initiative (ADNI). From the performed experiments, the proposed method achieves 93% accuracy for multiclass AD-MCI-NC and good precision AD 93%, MCI 91%, and NC 95%, respectively. Our study also demonstrates that the proposed multiplane approach outperforms the single-plane approach.

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“…Neurological disorders can be diagnosed through brain imaging, gait pattern assessment, electromyogram, electrocardiogram signals analysis, etc. The recent research shows the evidence of parkinson's, Alzheimer disease diagnosis based on MRI tissue segmentation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of Neurodegenerative Diseases with Twin Layered CNN Using Gait Dynamics

2022

IJIES

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This study identifies the gait dynamics of Neurodegenerative disease (NDD) patients using a deep convolution neural network (CNN) approach. Human gait examination perceives individuals from the manner in which they walk, which gives clues in identifying specific disorders. Precise identification of the specific NDD assists the doctors to begin early treatment strategies. The physionet public database containing the gait dynamics of Parkinson's, Amyotrophic Lateral Sclerosis, Huntington's Diseases, and the healthy subjects is used in this work. The gait parameters such as left stride intervals, right stride intervals, left swing intervals, right swing intervals, left stance intervals and right stance intervals of each disease group are utilized. Deep Twin layered CNN (TLCNN) is applied as a feature extractor, which automatically extracts deep features from the input gait signals. Further statistical features are also measured for these inputs by implementing Fast Walsh Hadamard Transform method (FWHT). The tuned neighbourhood component analysis (TNCA) is utilized as a feature selection algorithm to select prime features from the extracted deep feature vector. The classification of each disease group is made with the selected features by the Random forest and Multi SVM machine learning approach, which gives the accuracy of 99.89%, and 97.06% in classification of NDD and healthy person. Thus, the proposed TLCNN_FWHT_TNCA_RF gait classification approach based on CNN and machine learning is accurate and reliable to classify the particular neurodegenerative disorder.

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Diagnosis of Alzheimer Disease Using Fast Independent Component Analysis and Otsu Multi-level Thresholding

Cited by 4 publications

References 11 publications

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Multiplane Convolutional Neural Network (Mp-CNN) for Alzheimer’s Disease Classification

Identification and Analysis of Neurodegenerative Diseases with Twin Layered CNN Using Gait Dynamics

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