AUTOMATED SPARSE REPRESENTATION-BASED CLASSIFICATION OF ECHOCARDIOGRAPHICALLY DETECTED INTRACARDIAC MASSES USING MACHINE LEARNING
DOI:
https://doi.org/10.29121/shodhkosh.v5.i2.2024.4279Abstract [English]
One important responsibility in the diagnosis of cardiac sickness is the identification of intracardiac masses in echocardiograms. For the purpose of improving diagnostic precision, a new fully automated sparse representation-based classification method is introduced for the detection of intracardiac tumours and thrombi in echocardiography. To find the mass area, first a region of interest is cut. After that, the speckle is removed while the anatomical structure is preserved using a new global denoising process. Afterwards, a modified active contour model and K-singular value decomposition are used to depict the mass's contour and its associated atrial wall. Lastly, in order to distinguish between two masses, a sparse representation classifier processes the motion, boundary, and texture data. For this purpose, we gather 97 clinical echocardiography sequences.
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Copyright (c) 2024 Prof. Salunke Shrikant Dadasaheb, Prof. Kharat Punam Sagar, Prof. Dhage Tanuja Shrikant, Prof. Kadam Swati Amol
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