Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …

Weed detection and classification are considered one of the most vital tools in identifying
and recognizing plants in agricultural fields. Recently, machine learning techniques have …

Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging
(MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing …

Rumex obtusifolius Linnaeus (R. obtu. L.) is one of the vital broad-leaved weeds in
grassland that needs removal. It affects dairy products and reduces their quality. Hand …

Cardiac function indices must be calculated using tracing from short-axis images in cine-
MRI. A 3D-CNN (convolutional neural network) that adds time series information to images …

Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to
diagnose heart disease. However, repetitive manual segmentation of these images requires …

Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance
Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required …

MRI segmentation and analysis are significant tasks in clinical cardiac computations. A
cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose …

Accurate LVEF measurement is important in clinical practice as it identifies patients who may
be in need of life-prolonging treatments. This paper presents a deep learning based …

Among the many deep learning methods, the convolutional neural network (CNN) model
has an excellent performance in image recognition. Research on identifying and classifying …