TY - GEN
T1 - Segmentation of low-grade gliomas using U-Net VGG16 with transfer learning
AU - Rasyid, Dwilaksana Abdullah
AU - Huang, Guan Hua
AU - Iriawan, Nur
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021/1/28
Y1 - 2021/1/28
N2 - 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.
AB - 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.
KW - Brain Tumor Segmentation
KW - LGG
KW - U-Net
KW - VGG16
UR - http://www.scopus.com/inward/record.url?scp=85103845355&partnerID=8YFLogxK
U2 - 10.1109/Confluence51648.2021.9377093
DO - 10.1109/Confluence51648.2021.9377093
M3 - Conference contribution
AN - SCOPUS:85103845355
T3 - Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering
SP - 393
EP - 398
BT - Proceedings of the Confluence 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Cloud Computing, Data Science and Engineering, Confluence 2021
Y2 - 28 January 2021 through 29 January 2021
ER -
