2019³â ÄÄÇ»ÅÍÁ¾ÇÕÇмú´ëȸ
Current Result Document : 2 / 2
ÇѱÛÁ¦¸ñ(Korean Title) |
Discriminating Tumor from PET Scan Images Using Multi-level Convolutional Neural Network |
¿µ¹®Á¦¸ñ(English Title) |
Discriminating Tumor from PET Scan Images Using Multi-level Convolutional Neural Network |
ÀúÀÚ(Author) |
Tien X. Dang
Thong-Van Huynh
Soo-Hyung Kim
Jung-Joon Min
Sae-Ryung Kang
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 46 NO. 01 PP. 0805 ~ 0807 (2019. 06) |
Çѱ۳»¿ë (Korean Abstract) |
|
¿µ¹®³»¿ë (English Abstract) |
Nowadays, many breakthroughs have been made in image classification, however, medical imaging classification still a challenge such as classification tumor and non-tumor images. Classification is the first step and it plays an important role in tumor segmentation task. In this paper, we propose a deep learning model for discriminating tumor and non-tumor in Positron Emission Tomography (PET) scan images. Particularly, we propose a new deep learning model called multi-level in convolutional neural networks (ML-Net), which is a combination of a hierarchy of features intended to improve the classification task. The proposed algorithm was evaluated on the PET dataset and received good accuracy. |
Å°¿öµå(Keyword) |
|
ÆÄÀÏ÷ºÎ |
PDF ´Ù¿î·Îµå
|