AIMC Topic: Neural Networks, Computer

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CT-Mamba: A hybrid convolutional State Space Model for low-dose CT denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-r...

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Computers in biology and medicine
Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia and forecasting Alzheimer's disease (AD). Some existing deep learning methods often depend on anatom...

Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

Computers in biology and medicine
BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respiratory patterns during sleep. This disruption can have a cascading effect on the body, potentially leading to complications in various organs, including ...

A dataset of microscopic spirometra mansoni for medical image segmentation.

Computers in biology and medicine
Accurate diagnosis of adult Spirometra mansoni infections remains challenging, due to the limited sensitivity and high cost associated with immunodiagnostic methods. Advances in computer vision suggest deep learning-based etiological image analysis c...

Enhancing cancer diagnostics through a novel deep learning-based semantic segmentation algorithm: A low-cost, high-speed, and accurate approach.

Computers in biology and medicine
Deep learning-based semantic segmentation approaches provide an efficient and automated means for cancer diagnosis and monitoring, which is important in clinical applications. However, implementing these approaches outside the experimental environmen...

Generative adversarial network augmented data for improved heart sound abnormality detection.

Computers in biology and medicine
The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 dataset has driven significant advancements in automated heart sound analysis using machine learning (ML) and deep learning (DL). However, these efforts are constrained by the dataset's limi...

BrainCHEF: Cross-Level Hypergraph Enhanced Fusion model for brain networks.

Computers in biology and medicine
Modeling the dynamic characteristics of functional brain networks is of great significance for uncovering the mechanisms of brain function. Although graph neural networks (GNNs) have achieved remarkable progress in the analysis of functional networks...

Towards a comprehensive characterization of arteries and veins in retinal imaging.

Computers in biology and medicine
Retinal fundus imaging is crucial for diagnosing and monitoring eye diseases, which are often linked to systemic health conditions such as diabetes and hypertension. Current deep learning techniques often narrowly focus on segmenting retinal blood ve...

DCBLSTM-Deep Convolutional Bidirectional Long Short-Term Memory neural network for Q8 secondary protein structure prediction.

Computers in biology and medicine
Protein secondary structure prediction involves determining a protein's secondary structure from its primary amino acid sequence, serving as a critical step toward tertiary structure prediction. This, in turn, is essential for applications in drug de...

Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

Computers in biology and medicine
BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep lea...