Abstract
Segmentation is one of the important and challenging tasks in medical image analysis. Region of Interest (RoI) needs to be segmented from the background in almost all types of medical images for further analysis. This assist doctors in perfect diagnosis of diseases. In this paper convolutional neural network (CNN) has been used for automatic segmentation of RoI in medical imaging modalities such as carotid artery ultrasound images. The results obtained through proposed CNN are compared with other machine learning algorithm such as support vector machine (SVM) and radial basis function (RBF). CNN exhibits an edge in performance with 10-fold cross validation over the other two networks with an accuracy of 99.3%. The results are also compared with other standard CNN architectures.
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