Big Data Classification of Ultrasound Doppler Scan Images Using a Decision Tree Classifier Based on Maximally Stable Region Feature Points

The classification of ultrasound scan images is important in monitoring the development prenatal and maternal structures. This paper proposes a big data system for Doppler that combines residual maximally stable extreme regions speeded up robust features (SURF) with decision tree classifier. algorithm first preprocesses before detecting extremal (MSER). A few essential are chosen from MSER regions, along region provides best Region Interest (ROI). SURF points represent detected using gradient estimated cumulative interest. To extract feature pixels surround points, Triangular Vertex Transform (TVT) transform used. classifier used to train extracted TVT features. proposed image validated performance parameters such as accuracy, specificity, precision, sensitivity, F1 score. For validation, large dataset 12,400 collected 1792 patients method has an F1score 94.12%, accuracy 93.57%, 97.96%, respectively. evaluation results show classifying better than other algorithms have been past.