AIMC Topic: Neural Networks, Computer

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Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Sensors (Basel, Switzerland)
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, ...

Enhancing knee osteoarthritis diagnosis with DMS: a novel dense multi-scale convolutional neural network approach.

Journal of orthopaedic surgery and research
BACKGROUND: Osteoarthritis (OA) of the knee is a prevalent chronic degenerative joint condition that is having a growing impact on a global scale., posing a challenge in diagnosis which is often reliant on time-consuming and error-prone visual analys...

Psoriasis severity assessment: Optimizing diagnostic models with deep learning.

Narra J
Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis s...

DDP-DAR: Network intrusion detection based on denoising diffusion probabilistic model and dual-attention residual network.

Neural networks : the official journal of the International Neural Network Society
Network intrusion detection (NID) is an effective manner to guarantee the security of cyberspace. However, the scale of normal network traffic is much larger than intrusion traffic (i.e., appearing data imbalance problem), which leads to the training...

CMFX: Cross-modal fusion network for RGB-X crowd counting.

Neural networks : the official journal of the International Neural Network Society
Currently, for obtaining more accurate counts, existing methods primarily utilize RGB images combined with features of complementary modality (X-modality) for counting. However, designing a model that can adapt to various sensors is still an unsolved...

Generative Adversarial Network Based Contrast Enhancement: Synthetic Contrast Brain Magnetic Resonance Imaging.

Academic radiology
RATIONALE AND OBJECTIVES: Magnetic resonance imaging (MRI) is a vital tool for diagnosing neurological disorders, frequently utilising gadolinium-based contrast agents (GBCAs) to enhance resolution and specificity. However, GBCAs present certain risk...

Multimodal autism detection: Deep hybrid model with improved feature level fusion.

Computer methods and programs in biomedicine
OBJECTIVE: Social communication difficulties are a characteristic of autism spectrum disorder (ASD), a neurodevelopmental condition. The earlier method of diagnosing autism largely relied on error-prone behavioral observation of symptoms. More intell...

Determining structures of RNA conformers using AFM and deep neural networks.

Nature
Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible...

Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bev...

Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.

BMC medical imaging
Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting...