Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Houria, Latifa; Belkhamsa, Noureddine; Cherfa, Assia; Cherfa, Yazid

doi:10.1002/ima.22824

Cited by 12 publications

(3 citation statements)

References 42 publications

(74 reference statements)

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“…To extract the local unimodal and deep multimodal features in [20], fused bag-of-features with speeded-up robust features and a modified AlexNet were used. This is used in DTI scalar measurements (fractional anisotropy and diffusivity metrics) and segmented GM from T1-weighted MRI images.…”

Section: Conventional Approachesmentioning

confidence: 99%

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Alshayeji

2024

Mach. Learn.: Sci. Technol.

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Machine learning techniques applied in neuroimaging have prompted researchers to build models for early diagnosis of brain illnesses such as Alzheimer’s disease (AD). Although this task is difficult, advanced deep-learning (DL) approaches can be used. These DL models are effective, but difficult to interpret, time-consuming, and resource-intensive. Therefore, neuroscientists are interested in employing novel, less complex structures such as transformers that have superior pattern-extraction capabilities. In this study, an automated framework for accurate AD diagnosis and precise stage identification was developed by employing vision transformers (ViTs) with fewer computational resources. ViT, which captures the global context as opposed to convolutional neural networks (CNNs) with local receptive fields, is more efficient for brain image processing than CNN because the brain is a highly complex network with connected parts. The self-attention mechanism in the ViT helps to achieve this goal. Magnetic resonance brain images belonging to four stages were utilized to develop the proposed model, which achieved 99.83% detection accuracy, 99.69% sensitivity, 99.88% specificity, and 0.17% misclassification rate. Moreover, to prove the ability of the model to generalize, the mean distances of the transformer blocks and attention heat maps were visualized to understand what the model learned from the MRI input image.

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Section: Conventional Approachesmentioning

confidence: 99%

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Alshayeji

2024

Mach. Learn.: Sci. Technol.

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“…Multimodal imaging systems find diverse applications across various fields due to their ability to provide comprehensive insights by combining different imaging techniques. Some of the key applications include medical diagnostics [1][2][3][4], biomedical research [5], environmental and earth sciences [6,7]. In addition, the detection content in the industry is becoming more and more complex, and the information obtained by a single sensor cannot meet the detection needs.…”

Section: Introductionmentioning

confidence: 99%

Design of a Multimodal Detection System Tested on Tea Impurity Detection

Kuang,

Yu,

Guo

et al. 2024

Remote Sensing

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A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.

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“…Some of the key applications include Medical Diagnostics. [1][2][3][4], Biomedical Research [5], Environmental and Earth Sciences [6,7] . In addition, the detection content in the industry is becoming more and more complex, and the information obtained by a single sensor can not meet the needs of detection.…”

Section: Introductionmentioning

confidence: 99%

Design of a Mulitmodal Detection System and Its First Application in Tea Selection Process

Kuang,

Yu,

Guo

et al. 2024

Preprint

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A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.

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Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Cited by 12 publications

References 42 publications

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Alzheimer’s disease detection and stage identification from magnetic resonance brain images using vision transformer

Design of a Multimodal Detection System Tested on Tea Impurity Detection

Design of a Mulitmodal Detection System and Its First Application in Tea Selection Process

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