ANFIS based decision support system for prenatal detection of Truncus Arteriosus congenital heart defect

Congenital Heart Disease or Defect (CHD) is one of the most crucial causes of neonatal mortality. According to the consensus reported by Cardiological society of India, CHD is responsible for around 10% of infant mortality in India. Clinical investigation of CHD is normally performed with ultrasound (US) imaging modality. It captures biological internal structures with improper boundary due to inherent speckle noise. The fetal heart particularly has thin wall chambers and hence this fact protrudes to be a main motivation to contrive a new Computer Aided Diagnostic Support System (CADSS) to diagnose prenatal CHD from US images. This proposed CADSS is the first framework implemented to diagnose the prenatal Truncus Arteriosus congenital heart defect (TACHD) from 2D US images. The system starts with pre-processing the clinical data-set utilizing Probabilistic Patch Based Maximum Likelihood Estimation (PPBMLE). Then the anatomical structures are highlighted from the pre-processed information, utilizing the Fuzzy Connectedness based image segmentation process. Then 32 diagnostic features are extracted by utilizing seven different feature extraction models. Amongst, a subset of potential features are selected by applying Fisher Discriminant Ratio (FDR) analysis. Finally, Adaptive Neuro Fuzzy Inference System (ANFIS) is built with the selected feature subset as classifier, to perceive and show clinical results of prenatal TACHD. The performance analysis of various classifiers is evaluated by using 10-fold cross validation process for the image data-set. Comparative results prove that the proposed classifier has the potential to produce the higher classification accuracy than existing classifiers.

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