AIMC Topic: Neural Networks, Computer

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LGDNet: local feature coupling global representations network for pulmonary nodules detection.

Medical & biological engineering & computing
Detection of suspicious pulmonary nodules from lung CT scans is a crucial task in computer-aided diagnosis (CAD) systems. In recent years, various deep learning-based approaches have been proposed and demonstrated significant potential for addressing...

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges.

Micron (Oxford, England : 1993)
Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the acc...

Migraine headache (MH) classification using machine learning methods with data augmentation.

Scientific reports
Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnos...

Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools.

Journal of environmental management
The study investigated the spatiotemporal relationship between surface hydrological variables and groundwater quality/quantity using geostatistical and AI tools. AI models were developed to estimate groundwater quality from ground-based measurements ...

Transformaer-based model for lung adenocarcinoma subtypes.

Medical physics
BACKGROUND: Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis,...

On the pitfalls of Batch Normalization for end-to-end video learning: A study on surgical workflow analysis.

Medical image analysis
Batch Normalization's (BN) unique property of depending on other samples in a batch is known to cause problems in several tasks, including sequence modeling. Yet, BN-related issues are hardly studied for long video understanding, despite the ubiquito...

Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis.

Sensors (Basel, Switzerland)
Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this...

Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Various sensing modalities, including external and internal sensors, have been employed in research on human activity recognition (HAR). Among these, internal sensors, particularly wearable technologies, hold significant promise due to their lightwei...

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.

Scientific reports
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-sca...

An interpretable shapelets-based method for myocardial infarction detection using dynamic learning and deep learning.

Physiological measurement
Myocardial infarction (MI) is a prevalent cardiovascular disease that contributes to global mortality rates. Timely diagnosis and treatment of MI are crucial in reducing its fatality rate. Currently, electrocardiography (ECG) serves as the primary to...