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Inadequate dataset learning for major depressive disorder MRI semantic classification
(IET Image Processing, 2022)
Predicting patients with major depression (MDD) is currently a difficult task. Magnetic resonance imaging (MRI) data analysis may provide insight into individual patient responses, allowing for more customized treatment ...
Simplified inverse filter tracked affective acoustic signals classification incorporating deep convolutional neural networks
(Applied Soft Computing, 2020-12)
Facial expressions, verbal, behavioral, such as limb movements, and physiological features are vital ways for affective human interactions. Researchers have given machines the ability to recognize affective communication ...
Emotion classification on eye-tracking and electroencephalograph fused signals employing deep gradient neural networks
(Elsevier Ltd, 2021)
Emotion produces complex neural processes and physiological changes under appropriate event stimulation. Physiological signals have the advantage of better reflecting a person's actual emotional state than facial expressions ...
Brain fMRI segmentation under emotion stimuli incorporating attention-based deep convolutional neural networks
(Applied Soft Computing, 2022)
Functional magnetic resonance imaging (fMRI) is widely used for clinical examinations, diagnosis, and treatment. By segmenting fMRI images, large-scale medical image data can be processed more efficiently. Most deep learning ...
Plantar pressure image classification employing residual-network model-based conditional generative adversarial networks: a comparison of normal, planus, and talipes equinovarus feet
(Springer Science and Business Media Deutschland GmbH, 2023)
The number of deep learning (DL) layers increases, and following the performance of computing nodes improvement, the output accuracy of deep neural networks (DNN) faces a bottleneck problem. The resident network (RN) based ...
Emotion stimuli-based surface electromyography signal classification employing Markov transition field and deep neural networks
(Elsevier B.V., 2022)
Surface electromyography (sEMG) has been widely used in clinical medicine, rehabilitation medicine, and intelligent robots. Currently, sEMG signal classification methods promoted the development and industrialization of ...