This paper proposes an ultrasound Doppler scan image big data classification approach that uses a selection process to estimate the best regions for extracting the feature of a faster region-based convolutional neural network (RCNN) network. This scheme initially pre-processes the Doppler scan images. From the pre-processed image, several maximally stable extremal regions (MSER) and residual regions are estimated. The residual region and a few of the regions selected from the stable regions are used to extract the features. A correlation-based approach is used to select the stable regions for extracting the features. The gradient values of selected regions are used to extract the triangular vertex transform-based features (TVT). The extracted TVT features are trained using the faster RCNN network to categorize the ultrasound Doppler scan image as the femur, brain, abdomen,cervix, thorax, and other regions. The evaluation metrics namely precision, recall, and F1-score are used to validate the algorithm. The proposed Doppler ultrasound classification approach provides a sensitivity, F1-score, precision, specificity, and accuracy of 96.13%, 94.74%, 94.26%, 98.82%, and 98.27% respectively.

S. Sandhya kumari,K.Sandhya Rani."Selection of MSER region based Ultrasound Doppler scan Image Big data classification using a faster RCNN network". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.9, Issue 10,pp.184-192, October - 2022, URL :https://ijcert.org/ems/ijcert_papers/V9I1001.pdf,

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