Jan 1, 2023 · Traditional approaches to recognizing depression have relied on manually crafted techniques to extract facial expressions, which have their limitations. To address these …

In this study, we developed a depression recognition method based on facial images and a deep convolutional neural network. Based on 2-dimensional images, this method quantified the …

Feb 1, 2023 · In this work, a hybrid model is proposed for depression detection using deep learning algorithms, which mainly combines textual features and audio features of patient's …

Jun 26, 2022 · In this study, a high-performance hybrid neural network depression detection method is proposed based on deep learning technology. Different from the previous studies, …

Jan 21, 2021 · In the present paper we propose an integrated framework – Deep Local Global Attention Convolutional Neural Network (DLGA-CNN) for depression recognition, which adopts …

Dec 4, 2020 · In this work, we propose a new automatic depression detection method utilizing speech signals and linguistic content from patient interviews.

To detect depression, we have proposed a system based on CNN, OpenCV, Haar Cascade Classifier. Haar Cascade Classifier is a machine learning algorithm used for face detection. …

Sep 27, 2024 · This study proposes a novel framework, KWHO-CNN, integrating a hybrid metaheuristic algorithm with Attention-Driven Convolutional Neural Networks (CNNs), to …

Sep 9, 2022 · The aim of this work is to present a method capable of objectively detecting episodes of depression through a two-dimensional convolutional neural network (2D-CNN), …

Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their …