Fetal Heart Disease (FHD) based Structural Heart Disorders (SHD) occur when certain features of the heart develop abnormally. These flaws may cause blood flow to circulate in the erroneous spot, slow down, or be utterly blocked. Heart Defects (HD) caused via FHD or disorders that primarily impact embryonic heart conditions are alternatively referred to as Congenital Heart Defects (CHD). Multiple prior investigation algorithms such as Multi-Resolution Convolutional Neural Network (MRCNN), Deep Convolutional Neural Network (DCNN), Faster-RCNN (FRCNN) and DANomaly Wgan-GP and Convolutional Neural Network (DGACNN) rendered in the detection of FHD. Yet, the models have endured several challenges due to fuzzy constraints and irrelevant adherence. The intended aim is to detect the dilemma of the fetal heart in UltraSound (US) images using two distinct tier methods. The initial tier detects the fetal heart chamber's walls and valves using the Convolutional Neural Network (CNN)-incorporated Visual Geometry Group 16 (VGG 16) technique for processing fetal ultrasound images, allowing it to detect and dissolve anomalies in heart walls. This initial investigation concerns improving the image's quality in each subsequent sequence, from lowest to most improved using the conventional Augmented Wiener Filtering (AWF) approach. Succeeding, an instance-level Region of Interest (ROI) segmentation for exploiting the feature mining approach will be carried out via spatial features masking and ground-truth labeling framework for septal defect diagnosis. The second tier determines the flaws in fetal heart blood flow size, structure and vessels utilizing Deep Recurrent Neural Network (DRNN) integrated with region-based texture characteristics Local-Binary-Pattern (LBP), Histogram-of-Oriented-Gradient (HOG) and the Bags Of Features (BOF) segmentation framework via image acquiring. In eventual, the histogram equalization enhancement algorithm with Median Modified Wiener Filter (MMWF) is enumerated to enhance the visual quality, tests for signal-to-noise ratio, rate of variations, and noise proportion for sorting the blood vessels of the input fetal image. The analyzed CNN’s VGG 16 and DRNN model’s efficiency via Matrix Laboratory (MATLAB) has detected the cardiac features both in normal and abnormal ranges with an overall accuracy of 99.89% and 98.7%.

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