AIMC Topic: Deep Learning

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Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.

Frontiers in endocrinology
OBJECTIVE: This study aims to conduct an in-depth analysis of diabetic foot ulcer (DFU) images using deep learning models, achieving automated segmentation and classification of the wounds, with the goal of exploring the application of artificial int...

Optimizing CNN for pavement distress detection via edge-enhanced multi-scale feature fusion.

PloS one
Traditional crack detection methods initially relied on manual observation, followed by instrument-assisted techniques. Today, road surface inspection leverages deep learning to achieve automated crack detection. However, in the domain of deep learni...

Global burden and future trends of head and neck cancer: a deep learning-based analysis (1980-2030).

PloS one
BACKGROUND: Head and neck cancer (HNC) becomes a vital global health burden. Accurate assessment of the disease burden plays an essential role in setting health priorities and guiding decision-making.

Deep learning-based improved side-channel attacks using data denoising and feature fusion.

PloS one
Deep learning, as a high-performance data analysis method, has demonstrated superior efficiency and accuracy in side-channel attacks compared to traditional methods. However, many existing models enhance accuracy by stacking network layers, leading t...

Utilizing a deep learning model based on BERT for identifying enhancers and their strength.

PloS one
An enhancer is a specific DNA sequence typically located within a gene at upstream or downstream position and serves as a pivotal element in the regulation of eukaryotic gene transcription. Therefore, the recognition of enhancers is highly significan...

Cross-institutional validation of a polar map-free 3D deep learning model for obstructive coronary artery disease prediction using myocardial perfusion imaging: insights into generalizability and bias.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep learning (DL) models for predicting obstructive coronary artery disease (CAD) using myocardial perfusion imaging (MPI) have shown potential for enhancing diagnostic accuracy. However, their ability to maintain consistent performance acr...

PILOT: Deep Siamese network with hybrid attention improves prediction of mutation impact on protein stability.

Neural networks : the official journal of the International Neural Network Society
Evaluating the mutation impact on protein stability (ΔΔG) is essential in the study of protein engineering and understanding molecular mechanisms of disease-associated mutations. Here, we propose a novel deep learning framework, PILOT, for improved p...

A comprehensive review of computational methods for Protein-DNA binding site prediction.

Analytical biochemistry
Accurately identifying protein-DNA binding sites is essential for understanding the molecular mechanisms underlying biological processes, which in turn facilitates advancements in drug discovery and design. While biochemical experiments provide the m...

PursuitNet: A deep learning model for predicting competitive pursuit-like behavior in mice.

Brain research
Predator-prey interactions exemplify adaptive intelligence refined by evolution, yet replicating these behaviors in artificial systems remains challenging. Here, we introduce PursuitNet, a deep learning framework specifically designed to model the co...

Advances in MRI optic nerve segmentation.

Multiple sclerosis and related disorders
Understanding optic nerve structure and monitoring changes within it can provide insights into neurodegenerative diseases like multiple sclerosis, in which optic nerves are often damaged by inflammatory episodes of optic neuritis. Over the past decad...