Why the study?
Existing automatic detection and classification techniques for intracardiac masses in echocardiograms had limitations in accuracy and error rates.
Does a Double Convolutional Neural Network (DCNN) classifier improve the detection and classification accuracy of intracardiac masses in echocardiogram images compared to conventional machine learning models?
Population
Echocardiogram images with intracardiac masses
Comparison
Proposed AVMF-MS-LBP based RBPNN approach vs sparse representation, SVM, SVM-PSO, ANN, and KCR
Design
Algorithm development and validation study
Authors
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May support automated intracardiac mass classification on echocardiography; hypothesis-generating and requires prospective validation before clinical use.
Does a Double Convolutional Neural Network (DCNN) classifier improve the detection and classification accuracy of intracardiac masses in echocardiogram images compared to conventional machine learning models?
A Double Convolutional Neural Network classifier can accurately detect and classify intracardiac masses (tumors and thrombi) from echocardiogram images, potentially assisting cardiologists in clinical diagnosis.
Manikandan et al. (2021) studied this question.
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