Segmentation of thyroid nodules using Improvised U-Net Architecture
Nayana R Shenoy1, Anand Jatti2
1Nayana R Shenoy, Asst. Prof, Dr. AIT, Bangalore.
2Dr. Anand Jatti, Associate Professor, RVCE, Bangalore.
Manuscript received on May 07, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 10, 2020. | PP: 56-60 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.H6142069820 | DOI: 10.35940/ijitee.H6142.069820
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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: Thyroid nodules are considered as most common disease found in adults and thyroid cancer has increased over the years rapidly. Further automatic segmentation for ultrasound image is quite difficult due to the image poor quality, hence several researcher have focused and observed that U-Net achieves significant performance in medical image segmentation. However U-net faces the problem of low resolution which causes smoothness in image, hence in this research work we have proposed improvised U-Net which helps in achieving the better performance. The main aim of this research work is to achieve the probable Region of Interest through segmentation with better efficiency. In order to achieve that Improvised U-Net develops two distinctive feature map i.e. High level feature Map and low level feature map to avoid the problem of low resolution. Further proposed model is evaluated considering the standard dataset based on performance metrics such as Dice Coefficient and True positive Rate. Moreover our model achieves better performance than the existing model.
Keywords: Thyroid cancer, Segmentation, Improvised U-Net, ROI.
Scope of the Article: Service Oriented Architectures