AIMC Topic: X-Rays

Clear Filters Showing 201 to 210 of 451 articles

Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.

Nature communications
The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such...

Pre-processing methods in chest X-ray image classification.

PloS one
BACKGROUND: The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramou...

A deep learning model (FociRad) for automated detection of γ-H2AX foci and radiation dose estimation.

Scientific reports
DNA double-strand breaks (DSBs) are the most lethal form of damage to cells from irradiation. γ-H2AX (phosphorylated form of H2AX histone variant) has become one of the most reliable and sensitive biomarkers of DNA DSBs. However, the γ-H2AX foci assa...

Automatic detection of pneumonia in chest X-ray images using textural features.

Computers in biology and medicine
Fast and accurate diagnosis is critical for the triage and management of pneumonia, particularly in the current scenario of a COVID-19 pandemic, where this pathology is a major symptom of the infection. With the objective of providing tools for that ...

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

Journal of healthcare engineering
Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the r...

Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost.

Radiography (London, England : 1995)
INTRODUCTION: In late 2019 and after the COVID-19 pandemic in the world, many researchers and scholars tried to provide methods for detecting COVID-19 cases. Accordingly, this study focused on identifying patients with COVID-19 from chest X-ray image...

A fully automated sex estimation for proximal femur X-ray images through deep learning detection and classification.

Legal medicine (Tokyo, Japan)
PURPOSE: To develop a fully automated deep learning pipeline using digital radiographs to detect the proximal femur region for accurate automated sex estimation.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic detection of stenosis on X-ray Coronary Angiography (XCA) images may help diagnose early coronary artery disease. Stenosis is manifested by a buildup of plaque in the arteries, decreasing the blood flow to the hear...

A ensemble methodology for automatic classification of chest X-rays using deep learning.

Computers in biology and medicine
Chest radiographies, or chest X-rays, are the most standard imaging exams used in daily hospitals. Responsible for assisting in detecting numerous pathologies and findings that directly interfere in the patient's life, this exam is therefore crucial ...