Segmentation of low-grade gliomas using U-Net VGG16 with transfer learning

Dwilaksana Abdullah Rasyid, Guan Hua Huang, Nur Iriawan

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

Around 2000 cases of gliomas are diagnosed every year in the US, representing 23.41 percent of all primary brain tumors. World Health Organization (WHO) grade II gliomas or Low-Grade Gliomas (LGG) are slow-growing brain tumors. LGG is a fatal disease of young adults (between 35 and 44 years of age). LGG can transform into High-Grade Gliomas (HGG) or WHO grades III and IV occurred in most patients and ultimately leading to death. General treatment for LGG patients is surgical resection, radiotherapy, and chemotherapy. Fluid-Attenuated Inversion Recovery (FLAIR) imaging is needed to determine the tumor location before doing surgical resection. We propose a combined architectural innovation of U-Net and VGG16 with transfer learning as a hybrid model for tumor segmentation. Employing the preoperative FLAIR imaging data of 110 patients with LGG from the Cancer Genome Atlas, this deep learning algorithm achieves a high result with the Dice Similarity Coefficient of 99% and the Area Under Curve (AUC) of 98%, better than the previous approach done by Buda, et al.

原文English
主出版物標題Proceedings of the Confluence 2021
主出版物子標題11th International Conference on Cloud Computing, Data Science and Engineering
發行者Institute of Electrical and Electronics Engineers Inc.
頁面393-398
頁數6
ISBN(電子)9780738131603
DOIs
出版狀態Published - 28 1月 2021
事件11th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2021 - Virtual, Nodia, India
持續時間: 28 1月 202129 1月 2021

出版系列

名字Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering

Conference

Conference11th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2021
國家/地區India
城市Virtual, Nodia
期間28/01/2129/01/21

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