Investigating the Volumetric Biomarkers of Alzheimer Disease: A State-of-the Art Review

Barpanda, Soubhagya Sankar

doi:10.1007/978-981-19-1018-0_50

Cited by 3 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the improvement of the MRI image quality to reduce the noises [17], the use of segmentation in a specific brain region [18], classification techniques with AdaBoost [19], and slice-based method [20]. In general, the entire slices of MRI images are composed of 100 to 250 slices [21], [22]. However, Lopez et al found that the results in classification improve the accuracy with several slices than using the entire slices [23].…”

Section: Million People Have Been Diagnosedmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

et al. 2023

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a major public health priority. Hippocampus is one of the most affected areas of the brain and is easily accessible as a biomarker using MRI images in machine learning for diagnosing AD. In machine learning, using entire MRI image slices showed lower accuracy for AD classification. We present the select slices method by landmarks on the hippocampus region in MRI images. This study aims to see which views of MRI images have higher accuracy for AD classification. Then, to get the value of three views and categories, we used multiclass classification with the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using Resnet50 and LeNet. The models were used in a total dataset of 4,500 MRI slices in three views and categories. Our study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification. Our method improves the accuracy of machine learning, and the coronal view showed higher accuracy. This method played a significant role in improving the accuracy of machine learning performance. The results for the coronal view were similar to the medical experts usually used to diagnose AD. We also found that LeNet models became the potential model for AD classification.

show abstract

Section: Million People Have Been Diagnosedmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…This aggregated feature map X passed through the actual spatial self-attention function, which performs (3 Â 3,) ReLu convolution operation (S1) to obtain spatial correlation among all the pixels, which in turn fed into a sigmoid (3 Â 3, 1) convolution (S2) layer to obtain the attention weight matrix. The whole operation is formulated in Equation (5).…”

Section: Spatial Attention Block (Sab)mentioning

confidence: 99%

“…For our work, we have considered segmenting the cerebrum tissues of the brain, whose quantification is a significant step in brain image analysis 4 . The morphological changes of GM, WM, and CSF substructures of the cerebrum serve as biomarkers for several neurological disorders, such as Alzheimer's and Parkinson's disease 5 . Despite immense research in neuroimaging, segmenting these substructures remind challenging due to their complex structures.…”

Section: Introductionmentioning

confidence: 99%

“…4 The morphological changes of GM, WM, and CSF substructures of the cerebrum serve as biomarkers for several neurological disorders, such as Alzheimer's and Parkinson's disease. 5 Despite immense research in neuroimaging, segmenting these substructures remind challenging due to their complex structures. Among all the imaging techniques in neuroimaging, the widely accepted one is magnetic resonance imaging (MRI).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Residual UNet with Dual Attention—An ensemble residual UNet with dual attention for multi‐modal and multi‐class brain MRI segmentation

Barpanda

2022

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

In this paper, we have proposed a variant of UNet for brain magnetic resonance imaging (MRI) segmentation. The proposed model, termed as Residual UNet with Dual Attention (RUDA), addresses the two significant challenges of UNet: extraction of the complex features with unclear boundaries and the problem of over-segmentation due to the redundancy caused by the skip connection usage. RUDA is constituted upon the residual blocks for extracting the complex structures. It Introduces attention into the skip connections to avoid redundancy and thereby the chance of over-segmentation. Our model segments brain MRI into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) regions, which are considered crucial informative substructures for diagnosing neurological disorders such as Alzheimer's. It has been implemented in an ensemble manner to accommodate the multi-sequence (T1-weighted, IR, and T2-FLAIR) scans. The empirical analysis shows that with an accuracy of 93.80%, RUDA outperforms the two baseline models: UNet (91.37%), ResUNet (91.44%).

show abstract

Deep Learning Applications in MRI-Based Detection of the Hippocampal Region for Alzheimer’s Diagnosis

Pusparani,

Lin,

Jan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Investigating the Volumetric Biomarkers of Alzheimer Disease: A State-of-the Art Review

Cited by 3 publications

References 15 publications

Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

Residual UNet with Dual Attention—An ensemble residual UNet with dual attention for multi‐modal and multi‐class brain MRI segmentation

Deep Learning Applications in MRI-Based Detection of the Hippocampal Region for Alzheimer’s Diagnosis

Contact Info

Product

Resources

About