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Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks
(2022-11)
The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that ...
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals
(2022-08)
Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the muscle ...
Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings
(2022-07-27)
Background: The purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to ...
Enhancing deep learning-based 3-lead ECG classification with heartbeat counting and demographic data integration
(2022-08-15)
Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally. The gold standard for identifying these heart problems is via electrocardiogram (ECG). ...
Phase recognition in contrast-enhanced CT scans based on deep learning and random sampling
(2022-01-31)
Purpose: A fully automated system for interpreting abdominal computed tomography (CT) scans with multiple phases of contrast enhancement requires accurate classification of the phases. Current approaches typically utilize ...
FedDRL: Deep reinforcement learning-based adaptive aggregation for non-IID data in federated learning
(2022-08-04)
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions ...
Image-based contextual pill recognition with medical knowledge graph assistance
(2022)
In many healthcare applications, accurately identifying pills from images captured under varying conditions has become increasingly crucial. Despite numerous attempts to employ deep learning methods for pill recognition, ...
Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
(2022-03-20)
Building an accurate computer-aided diagnosis system based on data-driven approaches requires a large amount of high-quality labeled data. In medical imaging analysis, multiple expert annotators often produce subjective ...
Multi-stream fusion for class incremental learning in pill image classification
(2022)
Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they ...
Learning from multiple expert annotators for enhancing anomaly detection in medical image analysis
(2023-04-10)
Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays ...