Performance evaluation of unpreprocessed and pre-processed ultrasound images of carotid artery using CNN algorithm

Abstract

Detection of plaques is an important task especially those which are prone to rupture and may dislocate to some other body parts. Also early detection reduces the risk of cardiac and cerebrovascular anomalies. Due to its wide availability and low cost, ultrasound images of carotid artery has the ability and potential to gain the preference over other resources for plaque detection and analysis in medical practice. However, the difficulty caused in automated techniques to identify plaques is significantly due to image noise, plaque size and the complex appearance of tissues comprising a plaque. So, in this paper, we have addressed this problem by using deep learning techniques such as CNN algorithm. Here we will build a CNN (Convolutional Neural Network) that will extract features from the dataset of images thereby giving detailed information which will help the clinicians to identify the abnormalities and constituents of different plaques in an image and report the image as “normal” or “abnormal”. In this paper we have used approximately 1000 images (JPG format) of 100 cases to process and has validated the proposed convolutional neural network (CNN). The process of cross-validation with the clinical assessment showed a correlation of about 0.75 for the detection of plaque. This results indicate the potential of deep learning techniques in medical fields, here, automatic detection of anomalies in carotid ultrasound images.