AIMC Topic: Neural Networks, Computer

Clear Filters Showing 5131 to 5140 of 31376 articles

Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification.

IEEE journal of biomedical and health informatics
Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and fir...

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

IEEE journal of biomedical and health informatics
Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehen...

PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging.

IEEE journal of biomedical and health informatics
Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle hetero...

EEGDepressionNet: A Novel Self Attention-Based Gated DenseNet With Hybrid Heuristic Adopted Mental Depression Detection Model Using EEG Signals.

IEEE journal of biomedical and health informatics
World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the hu...

Retinal Vessel Segmentation by a Transformer-U-Net Hybrid Model With Dual-Path Decoder.

IEEE journal of biomedical and health informatics
This paper introduces an effective and efficient framework for retinal vessel segmentation. First, we design a Transformer-CNN hybrid model in which a Transformer module is inserted inside the U-Net to capture long-range interactions. Second, we desi...

Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing.

IEEE journal of biomedical and health informatics
The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-...

IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only r...

ABR-Attention: An Attention-Based Model for Precisely Localizing Auditory Brainstem Response.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Auditory Brainstem Response (ABR) is an evoked potential in the brainstem's neural centers in response to sound stimuli. Clinically, characteristic waves, especially Wave V latency, extracted from ABR can objectively indicate auditory loss and diagno...

Brain tumor detection and segmentation using deep learning.

Magma (New York, N.Y.)
OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection alg...

Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Accurate activity classification is essential for the advancement of closed-loop control for left ventricular assist devices (LVADs), as it provides necessary feedback to adapt device operation to the patient's current state. Therefore, this study ai...