This paper exhibits a computer-aided diagnosis system for the severity analysis of mitral regurgitation (MR) and assesses the discriminatory capability of Daubechies wavelet-based texture modeling. The Daubechies wavelet family has been utilized for the image decomposition because of its approximate shift invariance property. Seven statistical texture features have been utilized after the decomposition of the image up to four levels and after that concatenated. A supervised classifier, support vector machine (SVM) has been utilized with 10-fold cross-validation approach. The highest classification accuracies are 99.12 ± 0.44 utilizing db2, 99.70 ± 0.29 utilizing db4, and 97.68 ± 1.04 utilizing db4 wavelet in A2C, A4C, and PLAX respectively. The exploratory outcomes show that the proposed algorithm is effective and db4 beat among the Daubechies wavelet family considered amid this audit for precise severity investigation of the MR images.

本文展示了一种用于二尖瓣反流 (MR) 严重程度分析的计算机辅助诊断系统，并评估了基于 Daubechies 小波的纹理建模的鉴别能力。Daubechies 小波族因其近似平移不变性而被用于图像分解。在将图像分解为四个级别并连接之后，使用了七个统计纹理特征。监督分类器、支持向量机 (SVM) 已与 10 折交叉验证方法一起使用。在 A2C、A4C 和 PLAX 中，使用 db2 的最高分类精度分别为 99.12 ± 0.44、使用 db4 的 99.70 ± 0.29 和使用 db4 小波的 97.68 ± 1.04。