Classifying a type of brain disorder in children: an effective fMRI based deep attempt

Abstract

<span>Recent advanced intelligent learning approaches that are based on using neural networks in medical diagnosing increased researcher expectations. In fact, the literature proved a straight-line relation of the exact needs and the achieved results. Accordingly, it encouraged promising directions of applying these approaches toward saving time and efforts. This paper proposes a novel hybrid deep learning framework that is based on the restricted boltzmann machines (RBM) and the contractive autoencoder (CA) to classify the brain disorder and healthy control cases in children less than 12 years. The RBM focuses on obtaining the discriminative set of high guided features in the classification process, while the CA provides the regularization and the robustness of features for optimal objectives. The proposed framework diagnosed children with autism recording accuracy of 91, 14% and proved enhancement compared to literature.</span>

Implementation of deep neural networks learning on unmanned aerial vehicle based remote-sensing

“…These algorithms give every pixel in the picture a class label so that it can be put into the right group. Deep convolutional neural networks (CNNs) [1]- [4] have been able to outperform many standard computer vision methods [5]- [7] in recent times owing to the more access to big datasets and advancements in computational capabilities. When used to the pixel-by-pixel labelling of pictures, CNNs provide results with a poor spatial resolution, even though they are becoming more effective at classification and categorization tasks.…”