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Found 13 papers, 1 papers with code
Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review
This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature.
M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images
Our proposed model segments cup and disc regions based on which the abnormalities in cup to disc ratio can be observed.
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
Variational Capsule Encoder
We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space.
Brain Tumor Survival Prediction using Radiomics Features
Thus, accurate survival prognosis is an important step in treatment planning.
Deep Learning for Musculoskeletal Image Analysis
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology.
Electroencephalography based Classification of Long-term Stress using Psychological Labeling
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals
The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.
Classification of Perceived Human Stress using Physiological Signals
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.
Medical Image Analysis using Convolutional Neural Networks: A Review
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network
A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images.
Medical Image Retrieval using Deep Convolutional Neural Network
The learned features and the classification results are used to retrieve medical images.
