The non-invasive whole-brain scans endure the aptitude to succor for diagnosing neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde "DarkASDNet", which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we preprocessed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which is surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method establish state-of-the-art accuracy of 94.70\% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.

中文翻译：

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DarkASDNet：使用深度神经网络在功能性MRI上对ASD进行分类

非侵入性全脑扫描能够成功诊断自闭症，痴呆和脑癌等神经精神疾病。由于可公开获得的数据集的局限性，对自闭症谱系障碍（ASD）的可评估分析在理论上具有挑战性。对于诊断或预后工具，功能性磁共振成像（fMRI）对神经影像研究中生物标志物的确认是肯定的，因为fMRI拾取了大脑与区域之间固有的连通性。在ASD中，有很多深入的研究，其中介绍了机器学习或深度学习方法，这些方法为基于fMRI数据的ASD预测提供了高级步骤。但是，最大的先行模型在操纵性能指标（如准确性，准确性，召回率和F1得分）方面的能力不足。为了克服这些问题，我们提出了一种前卫的“ DarkASDNet”，它具有从较低级别到较高级别提取特征并展现出令人鼓舞的结果的能力。在这项工作中，我们考虑了3D fMRI数据来预测ASD和典型对照（TC）之间的二进制分类。首先，我们通过采用适当的切片时间校正和归一化来预处理3D fMRI数据。然后，我们介绍了一种新颖的DarkASDNet，该标准已超过ASD分类的基准精度。我们模型的结果表明，我们提出的方法建立了94.70％的最新准确度，可以在ABIDE-I，NYU数据集中对ASD与TC进行分类。最后，我们通过执行评估指标（包括准确性，召回率，F1得分，ROC曲线和AUC得分）来构想我们的模型，并通过与最新文献描述区分开来证明我们的结果是合法的。拟议的DarkASDNet体系结构为使用fMRI处理的数据进行ASD分类提供了一种新颖的基准方法。