Smartphone based blood flow feature extraction and classification from Doppler spectrum images
Ultrasound (US) Doppler spectrograms have been widely used for diagnosing vascular obstructions. This paper presents an Android smartphone based new approach for detecting the blood flow condition based on the US Doppler spectrogram images. A set of 59 spectrograms acquired from a US Doppler system is processed to extract features, and these non-redundant features are fed into a supervised classifier to determine the normal and abnormal blood flow. The classification is performed using the k-nearest neighbors (k-NN), Support vector machine (SVM), Naive Bayes (NB) and Multilayer perception (MLP) based classifiers. The SVM based classifier has shown superior performance, having an accuracy of 86.4 %, with a sensitivity and specificity of 96.4 % and 77.4 % respectively. The complete technique is implemented as an Android application and the results show the efficacy of the presented approach for the automated diagnosis of arterial diseases.