Automated EEG-based screening of depression using deep convolutional neural network.

TL;DR: This review critically examines the role of Convolutional Neural Networks (CNNs) in automated diagnosis of neurological disorders, highlighting their potential to enhance accuracy, efficiency, and objectivity, while addressing limitations, ethical considerations, and future directions in medical diagnostics.

TL;DR: A novel framework combining Spiking Neural Networks and Long Short-Term Memory for Depression Identification using EEG Signals achieves exceptional accuracy in classifying individual depression levels.

TL;DR: It is proved that the proposed approach can compute the contributions to the classification results reliably if the processes of each input feature in a classifier are independent of one another and the parameterization of each process is identical, as in shared weights in convolutional neural networks.

TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

TL;DR: In this paper, a Parametric Rectified Linear Unit (PReLU) was proposed to improve model fitting with nearly zero extra computational cost and little overfitting risk, which achieved a 4.94% top-5 test error on ImageNet 2012 classification dataset.

TL;DR: This work proposes a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit and derives a robust initialization method that particularly considers the rectifier nonlinearities.

TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.