Sparse Inverse Covariance Analysis of human brain for Alzheimer's disease study

Patel, Rinkal; Li, Jun; Chen, Kewei; Reiman, Eric M.; Alexander, Gene E.; Ye, Jieping

doi:10.1109/iccme.2009.4906604

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…At the moment, the conventional diagnosis of AD is based on a thorough study and evaluation of clinical data by physicians, which includes the neuropsychological test of the Minimum Mental State Examination (MMSE) [ 2 ] and the electroencephalogram (EEG) of the electroencephalogram (EEG). Physiological examination [ 3 ], magnetic resonance imaging (MRI), positron emission tomography (PET), neuroimaging examination [ 4 ], and cerebrospinal fluid examination [ 5 ] are a few examples. While these procedures have produced satisfactory diagnostic findings, they are time consuming, labor expensive, and prone to some degree of subjectivity, and misinterpretation is still possible.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Rinkal Patel et. al [12] has designed neuro-imaging research on brain tumor images which was the most popular technique, where voxels are studied independently. The deep learning methodology named "Sparse Inverse Covariance Analysis" enables functionality in the brain field, with accuracy of 88.24%.…”

Section: Related Workmentioning

confidence: 99%

“…ii) Advantage of using Semantic nano-segmentation in the proposed research is that it effectively finds the seed points, without overlap. The proposed method groups pixels together with similar intensity while the existing technique deals with the localized seed point model as in [8], [12].…”

Section: Proposed Research Work -Contributionmentioning

confidence: 99%

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

DEEPA¹,

S²,

Geisa

2020

Preprint

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Background: In recent days, the field of nano-technology is becoming popular due to increased rate of accurate detection and effectiveness in clinical patients using Computer-Aided Diagnosis (CAD). The high rate of precision with accuracy and classification of brain tumor as benign or malignant can be achieved with nano-scale imaging technology. This helps to enhance the superiority of life for brain tumor diseased patients. Results: In this work, we propose the novel Semantic nano-segmentation for the detection of brain tumors even in nano scale range. Proposed Semantic Nano-segmentation based on Advanced - Convolutional Neural Networks will help the radiologists to find the brain cancer even at early stages with nodules at very smaller size. The proposed method of Advanced - Convolutional Neural Networks (A-CNN) uses ResNet-50. Here the nano-image is taken as input and tumor image is segmented using Semantic Nano-segmentation which carries an average dice and SSIM values to be 0.2133 and 0.9704 respectively. The accuracy of 93.2% and 92.7% is obtained by the proposed Semantic nano segmentation for benign and malignant tumor images respectively. A-CNN method of automatic classification has an average accuracy of 99.57% and 95.7% for benign and malignant images respectively. Conclusion: This novel nano-method is created for effective detection of tumor area in nanometers (nm) and thus evaluates the disease perfectly. Closeness of the Proposed method at ROC curve with reference to True Positive values indicates higher performance than other methods. Comparative analysis on ResNet-50 with testing and training data at rate of 90% -10%, 80%-20% and 70%-30% respectively is made which proves the effectiveness of the proposed work.

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A Comprehensive Review Analysis of Alzheimer’s Disorder Using Machine Learning Approach

Dutta

Mishra

2022

Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis

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Sparse Inverse Covariance Analysis of human brain for Alzheimer's disease study

Cited by 3 publications

References 6 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

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

A Comprehensive Review Analysis of Alzheimer’s Disorder Using Machine Learning Approach

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