PDF(Pubmed)
Abstract：
UNASSIGNED: Dyslexia is a neurological disorder that affects an individual\'s language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs.
UNASSIGNED: In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM.
UNASSIGNED: The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.
UNASSIGNED: In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM.
UNASSIGNED: The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.
摘要：
阅读障碍是一种神经系统疾病，影响个人的语言处理能力。早期护理和干预可以帮助阅读障碍者在学术和社会上取得成功。深度学习（DL）方法的最新发展促使研究人员建立阅读障碍检测模型（DDM）。DL方法促进了多模态数据的集成。然而,很少有基于多模态的DDM。
■在这项研究中，作者使用多模态数据构建了基于DL的DDM。挤压和激励（SE）集成的MobileNetV3模型，基于自我注意机制(SA)的EfficientNetB7模型，并开发了早期停止和基于SA的双向长短期记忆（Bi-LSTM）模型，以从磁共振成像（MRI）中提取特征，功能性MRI,和脑电图（EEG）数据。此外,作者使用Hyperband优化技术对LightGBM模型进行了微调，以使用提取的特征检测阅读障碍。包含FMRI的三个数据集，MRI,和EEG数据用于评估拟议的DDM的性能。
■这些发现支持了拟议的DDM在有限的计算资源下检测阅读障碍的重要性。所提出的模型优于现有的DDM，产生98.9%的最佳精度，98.6%,功能磁共振成像占98.8%，MRI,和EEG数据集，分别。医疗中心和教育机构可以从所提出的模型中受益，以在初始阶段识别阅读障碍。通过集成基于视觉变换器的特征提取，可以提高所提出模型的可解释性。
■在这项研究中，作者使用多模态数据构建了基于DL的DDM。挤压和激励（SE）集成的MobileNetV3模型，基于自我注意机制(SA)的EfficientNetB7模型，并开发了早期停止和基于SA的双向长短期记忆（Bi-LSTM）模型，以从磁共振成像（MRI）中提取特征，功能性MRI,和脑电图（EEG）数据。此外,作者使用Hyperband优化技术对LightGBM模型进行了微调，以使用提取的特征检测阅读障碍。包含FMRI的三个数据集，MRI,和EEG数据用于评估拟议的DDM的性能。
■这些发现支持了拟议的DDM在有限的计算资源下检测阅读障碍的重要性。所提出的模型优于现有的DDM，产生98.9%的最佳精度，98.6%,功能磁共振成像占98.8%，MRI,和EEG数据集，分别。医疗中心和教育机构可以从所提出的模型中受益，以在初始阶段识别阅读障碍。通过集成基于视觉变换器的特征提取，可以提高所提出模型的可解释性。
