@inproceedings{0ebf899d0ef844389fcddfd13cd93645,
title = "Thyroid Nodule Ultrasonic Imaging Segmentation Based on a Deep Learning Model and Data Augmentation",
abstract = "The segmentation of thyroid nodule ultrasonic image is a critical step for thyroid disease diagnosis. With the advent of medical big data, deep convolutional neural networks (DCNNs) have contributed to the analysis of medical image. However, there is still room for improving the accuracy of the result. In this paper, we employ several data pre-processing algorithms to amplify the feature of the original data as well as augment the whole dataset. Moreover, we use a deep learning model, improved DeepLab v3+ segmentation DCNN to achieve better training and prediction performance on thyroid nodule dataset. The results show that the dice similarity coefficient is measured to be 94.08% and accuracy is 97.91%, which reveals the advance nature of our system.",
keywords = "Convolutional Neural Network, Data Augmentation Thyroid Nodule, Deep Learning, Medical Image, Sematic Segmentation, Ultrasonic Image",
author = "Zihao Guo and Jianqiao Zhou and Di Zhao",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 ; Conference date: 12-06-2020 Through 14-06-2020",
year = "2020",
month = jun,
doi = "10.1109/ITNEC48623.2020.9085093",
language = "English",
series = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "549--554",
editor = "Bing Xu and Kefen Mou",
booktitle = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",
address = "United States",
}
