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Found 11 papers, 4 papers with code
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage.
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model.
Blind ECG Restoration by Operational Cycle-GANs
Usually, a set of such artifacts occur on the same ECG signal with varying severity and duration, and this makes an accurate diagnosis by machines or medical doctors extremely difficult.
Early Myocardial Infarction Detection over Multi-view Echocardiography
Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in.
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Our model is implemented as a pipeline consisting of a 2D CNN that performs data preprocessing by segmenting the LV chamber from the apical four-chamber (A4C) view, followed by a 3D CNN that performs a binary classification to detect if the segmented echocardiography shows signs of MI.
COVID-19 Infection Localization and Severity Grading from Chest X-ray Images
In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images.
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32. 4 million people suffer each year.
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their "maximum motion displacement" plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF).
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization
The accuracy, precision, sensitivity, F1-score, specificity in the detection of tuberculosis using X-ray images were 97. 07 %, 97. 34 %, 97. 07 %, 97. 14 % and 97. 36 % respectively.
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
