Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This is because the CNN learns features similarly to human brain where it preserves local structure and avoids distortion of the global feature space. Focusing on the achievements of using the CNN for the fMRI, and accordingly, the Deep Convolutional Auto-Encoder (DCAE) benefits from the data-driven approach with CNN’s optimal features to strengthen the fMRI classification. In this paper, a new two consequent multi-layers DCAE deep discriminative approach for classifying fMRI Images is proposed. The first DCAE is unsupervised sub-model that is composed of four CNN. It focuses on learning weights to utilize discriminative characteristics of the extracted features for robust reconstruction of fMRI with lower dimensional considering tiny details and refining by its deep multiple layers. Then the second DCAE is a supervised sub-model that focuses on training labels to reach an outperformed results. The proposed approach proved its effectiveness and improved literately reported results on a large brain disorder fMRI dataset.

中文翻译：

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一种用于对 fMRI 图像进行分类的两个后续多层深度判别方法

功能性磁共振成像 (fMRI) 几十年来一直是诊断医疗问题的潜在辅助方法。文献中已经提出了几种成功的机器学习算法来从 fMRI 中提取有价值的知识。其中一种算法是卷积神经网络 (CNN)，它具有学习 fMRI 最佳抽象的强大能力。这是因为 CNN 学习的特征类似于人脑，它保留了局部结构并避免了全局特征空间的失真。专注于将 CNN 用于 fMRI 的成就，因此，深度卷积自动编码器 (DCAE) 受益于具有 CNN 最佳特征的数据驱动方法，以加强 fMRI 分类。在本文中，提出了一种新的用于对 fMRI 图像进行分类的两层连续多层 DCAE 深度判别方法。第一个 DCAE 是由四个 CNN 组成的无监督子模型。它侧重于学习权重，以利用提取特征的判别特征来稳健地重建 fMRI，考虑到微小细节并通过其深层多层进行细化。然后第二个 DCAE 是一个有监督的子模型，专注于训练标签以达到更好的结果。所提出的方法证明了它的有效性，并在大型脑疾病 fMRI 数据集上改进了文字报告的结果。它侧重于学习权重，以利用提取特征的判别特征来稳健地重建 fMRI，考虑到微小细节并通过其深层多层进行细化。然后第二个 DCAE 是一个有监督的子模型，专注于训练标签以达到更好的结果。所提出的方法证明了它的有效性，并在大型脑疾病 fMRI 数据集上改进了文字报告的结果。它侧重于学习权重，以利用提取特征的判别特征来稳健地重建 fMRI，考虑到微小细节并通过其深层多层进行细化。然后第二个 DCAE 是一个有监督的子模型，专注于训练标签以达到更好的结果。所提出的方法证明了它的有效性，并在大型脑疾病 fMRI 数据集上改进了文字报告的结果。