Detection of ASD Children through Deep-Learning Application of fMRI

Feng, Min; Xu, Juncai

doi:10.3390/children10101654

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…A CNN is composed of several types of layers, each performing a specific operation on the input data. These layers include convolutional layers, pooling layers, fully connected layers, and a classifier layer [35], [36].…”

Section: The Cnn Depth Networkmentioning

confidence: 99%

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

Feng,

2024

J. meas. eng.

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This research introduces a novel approach for detecting defects in concrete structures. It utilizes the Gramian Angular Difference Field (GADF) in combination with a Convolutional Neural Network (CNN) enhanced by depthwise separable convolutions and attention mechanisms. The key contribution of this work is the use of GADF to transform one-dimensional impact-echo signals into two-dimensional images, thereby improving feature extraction and computational efficiency for analysis by the CNN. This advancement offers a new perspective in non-destructive testing technologies for concrete infrastructure. Comprehensive evaluation on a varied dataset of concrete structural defects reveals that our GADF-CNN model achieves an impressive test accuracy of 98.24 %, surpassing conventional models like VGG16, ResNet18, DenseNet, and ResNeXt50, and excelling in precision, recall, and F1-score metrics. Ultimately, this study enhances the integration of sophisticated image transformation techniques with deep learning, contributing to safer and more durable concrete infrastructure, and represents a noteworthy development in the field.

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Section: The Cnn Depth Networkmentioning

confidence: 99%

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

Feng,

2024

J. meas. eng.

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“…On the other hand, AI, as also described above, particularly machine learning, enables sophisticated analysis of vast and complex datasets, identifying subtle patterns in individual responses. Together, these arms create a powerful synergy, enhancing our understanding of autism's neural underpinnings, personalizing interventions, and guiding the development of effective treatments [56,57].…”

Section: Integrating the Two Tools Of Ai And Fmri In Autismmentioning

confidence: 99%

“…Integrating fMRI with AI [56,57] can help identify complex patterns in brain activity in autism and predict individual behavior, providing valuable information for more precise diagnosis and personalized intervention strategies. In summary, autism is a complex neurodevelopmental disorder with a wide variety of manifestations.…”

Section: Integrating the Two Tools Of Ai And Fmri In Autismmentioning

confidence: 99%

An Umbrella Review of the Fusion of fMRI and AI in Autism

Giansanti

2023

Diagnostics

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The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study’s objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.

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“…ADHD affects approximately 5-10% of school-aged children worldwide [3,4], and early diagnosis and intervention are essential to improve the quality of life of patients and their families [5,6]. However, traditional diagnostic methods such as clinical interviews, behavioral observations, and rating scales can be subjective and time-consuming [7][8][9], highlighting the need for objective and valid diagnostic tools.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Feng,

2024

Brain Sciences

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Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.

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Detection of ASD Children through Deep-Learning Application of fMRI

Cited by 7 publications

References 42 publications

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

Enhancing non-destructive testing in concrete structures: a GADF-CNN approach for defect detection

An Umbrella Review of the Fusion of fMRI and AI in Autism

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

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