AUTOMATED SPARSE REPRESENTATION-BASED CLASSIFICATION OF ECHOCARDIOGRAPHICALLY DETECTED INTRACARDIAC MASSES USING MACHINE LEARNING - Salunke, Shrikant Dadasaheb; Kharat, Punam Sagar; Dhage, Tanuja Shrikant; Kadam, Swati Amol (ShodhKosh: Journal of Visual and Performing Arts, 2024)
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.