Ultrasound image segmentation through deep learning based improvised U-Net - Nayana R. Shenoy; Anand Jatti (Institute of Advanced Engineering and Science, 2021)
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism, however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.