MRI Segmentation of Brain Tissue and Course Classification in Alzheimer’s Disease

Li, Meimei; Hu, Chunhai; Liu, Zhen; Zhou, Ying

doi:10.3390/electronics11081288

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is noted that the results in Table 6 show comparisons based on the entire algorithms, while the results in Table 2 show the comparisons based on the different deep learning architectures. There are several methods [ 71 , 72 , 73 , 74 , 75 ] using the Clinical and BrainWeb datasets [ 76 ]. These CNN methods [ 71 , 72 , 73 , 74 , 75 ] provide segmentation accuracy from 0.85 to 0.94.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…There are several methods [ 71 , 72 , 73 , 74 , 75 ] using the Clinical and BrainWeb datasets [ 76 ]. These CNN methods [ 71 , 72 , 73 , 74 , 75 ] provide segmentation accuracy from 0.85 to 0.94. Due to the difference in experimental conditions, the segmentation results details are not included in this paper.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

Yamanakkanavar

Choi

Lee

2022

Sensors

View full text Add to dashboard Cite

In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections, and squeeze–expand convolutional layers from the fire module to segment brain MRI data. The proposed SM-SegNet architecture involves a multi-scale deep network on the encoder side and deep supervision on the decoder side, which uses combined-connections (skip connections and pooling indices) from the encoder to the decoder layer. The multi-scale side input layers support the deep network layers’ extraction of discriminative feature information, and the decoder side provides deep supervision to reduce the gradient problem. By using combined-connections, extracted features can be transferred from the encoder to the decoder resulting in recovering spatial information, which makes the model converge faster. Long skip connections were used to stabilize the gradient updates in the network. Owing to the adoption of the fire module, the proposed model was significantly faster to train and offered a more efficient memory usage with 83% fewer parameters than previously developed methods, owing to the adoption of the fire module. The proposed method was evaluated using the open-access series of imaging studies (OASIS) and the internet brain segmentation registry (IBSR) datasets. The experimental results demonstrate that the proposed SM-SegNet architecture achieves segmentation accuracies of 95% for cerebrospinal fluid, 95% for gray matter, and 96% for white matter, which outperforms the existing methods in both subjective and objective metrics in brain MRI segmentation.

show abstract

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

Yamanakkanavar

Choi

Lee

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Li et al [25] employed the MultiRes + UNet network to successfully segregate the brain tissue. With the use of MultiRes blocks and Res path structures, the authors reduced the amount of memory in the network.…”

Section: Tissue Segmentationmentioning

confidence: 99%

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

M.¹,

Rao²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Neurological disorders such as Alzheimer's disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five classes. Initially, a network based on Unet++ is built to segment the tissues of the brain. Then, using the segmented tissue components as input, the Xception-based deep learning technique is employed to extract high-level features. Finally, the optimized Fractalnet framework is used to categorize the disease condition using the acquired characteristics. The proposed strategy is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset that accurately segments brain tissues with a 98.45% of dice similarity coefficient (DSC). Additionally, for the multiclass classification of AD, the suggested technique obtains an accuracy of 99.06%. Moreover, ANOVA statistical analysis is also used to evaluate if the groups are significant or not. The findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.

show abstract

A Review of AI techniques using MRI Brain Images for Alzheimer's disease detection

Ali

Sadek

Makki

2023

2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)

View full text Add to dashboard Cite

MRI Segmentation of Brain Tissue and Course Classification in Alzheimer’s Disease

Cited by 3 publications

References 36 publications

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

A Review of AI techniques using MRI Brain Images for Alzheimer's disease detection

Contact Info

Product

Resources

About