Rajaram Pavithra, Marimuthu Mohanapriya
CSE, Coimbatore Institute of Technology, Coimbatore, India.
Network. 2025 Aug;36(3):1221-1252. doi: 10.1080/0954898X.2024.2412679. Epub 2024 Oct 13.
Autism Spectrum Disorder (ASD) poses a significant challenge in early diagnosis and intervention due to its multifaceted clinical presentation and lack of objective biomarkers. This research presents a novel approach, termed Neuro Connect, which integrates data-driven techniques with Bidirectional Gated Recurrent Unit (BiGRU) classification to enhance the prediction of ASD using functional Magnetic Resonance Imaging (fMRI) data. This study uses both structural and functional neuroimaging data to investigate the complex brain underpinnings of autism spectrum disorder (ASD). They use an Auto-Encoder (AE) to efficiently reduce dimensionality while retaining critical information by learning and compressing important characteristics from high-dimensional data. We treat the feature-extracted data using a BiGRU model for the classification task of predicting ASD. They provide a new optimization strategy, the Horse Herd Algorithm (HHA), and show that it outperforms other established optimizers, such SGD and Adam, in order to improve classification accuracy. The model's performance is greatly enhanced by the HHA's novel optimization technique, which more precisely refines weight modifications made during training. The proposed ASD and EEG dataset accuracy value is 99.5%, and 99.3 compared to the existing method the proposed has a high accuracy value.
自闭症谱系障碍（ASD）因其多方面的临床表现和缺乏客观生物标志物，在早期诊断和干预方面构成了重大挑战。本研究提出了一种名为Neuro Connect的新方法，该方法将数据驱动技术与双向门控循环单元（BiGRU）分类相结合，以利用功能磁共振成像（fMRI）数据增强对ASD的预测。本研究使用结构和功能神经影像数据来研究自闭症谱系障碍（ASD）复杂的大脑基础。他们使用自动编码器（AE）通过从高维数据中学习和压缩重要特征，在保留关键信息的同时有效地降低维度。我们使用BiGRU模型对特征提取后的数据进行处理，以完成预测ASD的分类任务。他们提供了一种新的优化策略——马群算法（HHA），并表明在提高分类准确率方面，它优于其他既定的优化器，如随机梯度下降（SGD）和亚当（Adam）优化器。HHA的新型优化技术极大地提高了模型的性能，该技术更精确地优化了训练期间的权重调整。所提出方法针对ASD和脑电图数据集的准确率值为99.5%，与现有方法相比，所提出方法的准确率值为99.3%，具有较高的准确率。
