AIMC Topic: Neural Networks, Computer

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Fuzzy guided ensemble inference system for brain tumor classification.

Brain research
The abnormal growth of cells inside or near the brain is called a brain tumor. Brain tumors can be benign (non-cancerous) or malignant (cancerous). Both these types can exert pressure on the surrounding brain tissue, increasing intracranial pressure....

Atlas-independent brain connectome analysis at voxel-level granularity: graph convolutional networks for etiology classification in newborns.

NeuroImage
Early identification of altered brain networks in neonates at risk for neurodevelopmental impairments is critical for timely intervention and improving outcomes. This study explores the potential of graph neural networks (GNNs) applied to structural ...

Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.

BMC oral health
BACKGROUND: Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires ...

Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based on F-FDG PET imaging and deep learning.

BMC neurology
OBJECTIVE: Freezing of gait (FOG) is a common gait disorder in the advanced stages of Parkinson's disease (PD), closely associated with impaired balance and executive function. This study aimed to investigate specific changes in brain glucose metabol...

IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases.

Scientific reports
In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs)...

Deep learning for motion classification in ankle exoskeletons using surface EMG and IMU signals.

Scientific reports
Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility, support rehabilitation, and reduce fall risks, particularly among the aging population. Their effectiveness depends on accurate, real-time prediction of u...

DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.

Scientific reports
Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learni...

Machine learning methods on BioVid heat pain database for pain intensity estimation.

Scientific reports
Pain assessment is a critical aspect of medical practice, directly influencing patient treatment and quality of life. Traditional pain evaluation methods, such as the Numerical Rating Scale (NRS), Visual Analog Scale (VAS), and Verbal Rating Scale (V...

ClairS-TO: a deep-learning method for long-read tumor-only somatic small variant calling.

Nature communications
Accurate detection of somatic variants in tumors is of critical importance and remains challenging. Current methods typically require matched normal samples for reliable detection, which are often unavailable in real-world research and clinical scena...

A review of the application of deep learning in thyroid nodule imaging: from model architectures to training methods and core image analysis tasks.

Biomedical physics & engineering express
Thyroid nodules are highly prevalent in clinical practice, and their incidence has been steadily increasing in recent years, posing significant threats to human health. Traditional imaging examinations for thyroid nodules rely heavily on physicians' ...