FETAL 3D-ECHO CLASSIFICATION AND SEGMENTATION USING COLOR AND TEXTURAL FEATURES FOR TR DETECTION

Abstract

Tricuspid regurgitation (TR) is a condition in which the valve between the right atrium and right ventricle does not close properly. Therefore, blood leaks backward into the upper right chamber. Most commonly, tricuspid regurgitation is caused by an enlarged right ventricle. In this paper, a novel deep learning technique called Tricuspid regurgitation identification in the fetal heart (TRI-FH) approach has been proposed, for identifying TR in the early stages. The gathered 3D-echo images are pre-processed to improve the quality of the images. The feature extraction techniques namely color-level co-occurrence matrix (CLCM) and grey-level co-occurrence matrix (GLCM) are applied on both RGB and grey images. The extracted features are extracted using the wrapper method and the unsupervised dimension reduction technique namely independent component analysis (ICA) is used to reduce the dimension of extracted features. Afterward, the deep learning-based Ghost network is used for classifying normal, Mild TR, and severe TR cases. The classified TR cases are fed to the segmentation phase for segmenting the affected valve of the fetus. The experimental TRI-FH approach achieves a total accuracy of 98.07 %. As compared to existing techniques, the proposed TRI-FH method shows higher performance in terms of accuracy, precision, recall, specificity, and F1 score. The proposed TRI-FH model enhances the total accuracy by 95.64 %, 97.82 %, 93.21 %, and 95.67 % better than ResNet, DenseNet, LinkNet, and U-Net respectively.