Detection of Abnormal Tumor Regions in Ultrasonic Thyroid Images using SVM Classification Method
B. Shankarlal1, P. D. Sathya2
1B. Shankarlal*, Assistant Professor, Department of ECE, PKIET, Karaikal, India.
2Dr. P. D. Sathya, Assistant Professor, Department of ECE, Annamalai University, Chidambaram, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3079-3081 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6078029320/2020©BEIESP | DOI: 10.35940/ijeat.C6078.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Tumor, thyroid, images, SVM, mode.