FHD deep learning prognosis approach: Early detection of fetal heart disease (FHD) using ultrasonography image-based IROI combined multiresolution DCNN.

Abstract

Fetal Heart Disease (FHD) is the most prevalent root cause of infant demise which accounts for 21% of all congenital abnormalities, with most instances being catastrophic, thereby rendering the need for early prognosis. Ultrasonography is the forefront imaging modality for assessing fetal growth in four-chamber and blood vessel malformation. Clinically diagnosing the abnormality is time-consuming and requires the skill of a radiologist. In subsequent, numerous preceding research strategies ideal to meta-heuristic and deep learning's Faster Artificial Neural Network (FANN), Dense Recurrent Neural Network (DRNN), Mask-Regional Convolution Neural Network (M RCNN) and Enhanced Deep Learning-assisted CNN aid in the identification of FHD. However, the prediction models have encountered multiple challenges owing to imprecise hinders and irrelevant adhesion. Hence, we propose the automated hierarchical network-driven findings of FHD in four-chamber and blood vessels using ultrasonic 2D imaging which undergoes 3 consequential processes of Enhanced-Adaptive Median Filtering (EAMF) pre-process concerning noise variations i.e., test for SNR distortion and image enhancement i.e., visual quality, Intensified Region of Interest (IROI) segmentation for exploiting feature selection via spatial mask-labeling and Multiresolution Deep Convolutional Neural Network (MDCNN) classification in the detection of diseased pattern via confusion metrics (CM). The lesion findings of CM is determined using MATLAB R2023b with an overall substantial efficiency of 99.79% in both normal and abnormal conditions with a significant potential to assist cardiologists in the prognosis of FHD.