FPGA based Abnormality Classification in Kidney Ultrasound Images using KNN

Abstract

Ultrasound imaging has been widely used for diagnosing the internal abnormalities. Tele-radiographers are lacking nowadays to provide accurate diagnosis of the presence of abnormalities. To overcome this issue, Teleradiology paved a new way for the online access of the doctors around all parts of the world. Frequent access through online is a bottleneck problem. So, CAD based technique is implemented in VIRTEX-6 FPGA platform in order to clearly classify the presence of the abnormality. Manual intervention is reduced in this case. The proposed algorithm involves five steps: pre-processing segmentation, feature extraction, feature selection and classification. Based on selected features, the classification is made using KNN Classifier. Good range of specificity is achieved which is comparable with the existing algorithms. The specificity for normal, cystic and stone kidneys is 95, 80 and 75 respectively. The proposed method produces far higher accuracy when compared with SVM using linear, RBF and polynomial kernel. But it shows a bit lesser value when compared with SVM using MLP kernel.