Detection of Tumors in Ultra Sound Thyroid Images using Random Forest 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 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2193-2195 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1735029420/2020©BEIESP | DOI: 10.35940/ijitee.D1735.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: The thyroid gland is important for balancing the hormones in our body for our daily routine activity. This paper detects the tumor regions in ultrasound thyroid image using feature extractions based Random Forest (RF) classification approach. In this paper, Curvelet transform is used to transform the pixels associated with spatial into the pixels associated with frequency. In this paper, Random Forest (RF) classification algorithm is used for the classification of the computed features from the thyroid image.
Keywords: Thyroid, gland, tumor, Curvelet, classifications.
Scope of the Article: Classifications