AIMC Topic: Neural Networks, Computer

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Segmentation of Structural Components of Atherosclerotic Plaques on OCT Images Using Deep Machine Learning.

Kardiologiia
Aim        To develop an optimal method for automated segmentation of atherosclerotic plaque structural components in optical coherence tomography (OCT) images using an ensemble of deep learning neural network models based on a comparison of nine art...

Neural subgraph counting on stream graphs via localized updates and monotonic learning.

PloS one
Graphs are a representative type of fundamental data structures. They are capable of representing complex association relationships in diverse domains. For large-scale graph processing, the stream graphs have become efficient tools to process dynamic...

Efficient deep neural networks for cancer detection on histopathology combining attention and image downsampling.

Scientific reports
Pathology diagnosis of colorectal cancer is time-consuming and requires a high level of expertise. However, it is an essential step towards establishing the adequate treatment. The need to analyse a large number of these histopathological images call...

Early detection of self-care impairments in children with disabilities using an enhanced SE network optimized by ISCO algorithm.

Scientific reports
Children with disabilities frequently encounter considerable obstacles in acquiring self-care skills, which are vigorous for developing their independence and overall quality of life. The early detection of self-care deficits is important for prompt ...

Fine-tuned ResNet34 for efficient brain tumor classification.

Scientific reports
Brain tumors are among the most fatal diseases, Often leading to a reduction in life expectancy. Early and accurate diagnosis is essential to guide effective treatment and enhance survival rates. Advances in artificial intelligence, particularly deep...

An enhanced blood-sucking leech optimization for training feedforward neural networks.

Scientific reports
The input, hidden and output layers cultivate a hierarchical framework of the feedforward neural networks (FNNs) characterized by unidirectional information flow and feedback feedback-free loop connection, the network highlights attributes of fortifi...

Grape sugar content prediction with multispectral alignment and improved residual network.

Scientific reports
Sugar content is a crucial indicator of grape ripeness and grading, and developing non-contact and non-destructive sugar content detection devices is essential for grape-picking robots and sorting platforms. Spectroscopy, which can detect the chemica...

Automated AI detection of thoracic aortic dissection on CT imaging.

European radiology experimental
BACKGROUND: Aortic dissection (AD) is a life-threatening condition. We developed an artificial intelligence (AI) algorithm capable of robust, accurate, and automated AD detection and sub-classification.

Portable AI-driven electrochemical aptasensor for real-time Staphylococcus aureus detection in food and beverages.

Mikrochimica acta
A portable electrochemical aptasensor integrated with machine learning was developed for rapid and on-site detection of Staphylococcus aureus (S. aureus) in food and beverage samples. The aptasensor was fabricated using screen-printed carbon electrod...

Establishment of an Infrared-Camera-Based Home-Cage Tracking System Goblotrop.

eNeuro
Studying locomotor activity in animal models is crucial for understanding physiological, behavioral, and pathological processes. This study aimed to develop an artificial intelligence-based tracking system called Goblotrop, designed to localize roden...