Why the study?
Identification and classification of intracardiac masses in echocardiogram is a significant process in diagnosing cardiovascular disease, but conventional echocardiogram image analysis faces multiple issues.
A novel neural network-based approach for classifying intracardiac masses in echocardiograms demonstrated high accuracy, potentially aiding in the automated diagnosis of cardiac tumors.
Automated intracardiac mass classification on imaging shows high accuracy; leaves open prospective clinical validation before adoption.
Identification and classification of intracardiac masses in echocardiogram is one of the significant processes in the diagnosis of cardiovascular disease. A robust back propagation neural network (RBPNN) technique is used to conquer every single conventional-issue utilizing the echocardiogram image analysis for this work, which consists of four phases such as noise removal, automatic segmentation, feature extraction, and intracardiac masses classification. Initially, the noise is diminished from the echocardiogram images utilizing the adaptive vector median filter (AVMF). Then, linear iterative vessel segmentation (LIVS) is applied for automatic segmentation of the masses followed by the extraction of texture features using the multiscale local binary pattern (MS-LBP) approach. Finally, RBPNN is employed to classify the heart mass from the images of echocardiogram with the layered kernel for the system combination. Extensive simulation results obtained using proposed AVMF-MS-LBP based RBPNN approach disclosed the superiority over existing intracardiac mass detection and classification approaches in terms of accuracy of 98.85%.
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A. Manikandan (2022) studied this question.
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