Open AccessDetection of Abnormal Tumor Regions in Ultrasonic Thyroid Images using SVM Classification Method
Author(s) -
B. Shankarlal,
P. D. Sathya
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6078.029320
Subject(s) - thyroid , artificial intelligence , support vector machine , pattern recognition (psychology) , segmentation , computer science , image segmentation , computer vision , medicine
Detection of tumor or abnormal regions in thyroid gland is difficult task in human. The following methods are presently used for detecting the abnormal regions in thyroid gland as blood test, sample testing from thyroid gland and image processing method. This paper develops a methodology to detect the tumor regions in thyroid images using image registration and image enhancement technique. The Support Vector Machine (SVM) classifier is operated in two modes as training pattern generation and testing mode. The generation of training pattern from both normal and abnormal ultrasonic thyroid images. This proposed method for thyroid tumor region detection obtains 96.54% of sensitivity, 97.57% of specificity and 98.56% of average tumor segmentation accuracy.