Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
»ïÂ÷¿ø ½ÉÃþ Äܺ¼·ç¼Ç ½Å°æ¸ÁÀ» ÀÌ¿ëÇÑ ÄÄÇ»ÅÍ ´ÜÃþ ÃÔ¿µ ¿µ»ó ³» Æó °áÀý ºÐ·ù |
¿µ¹®Á¦¸ñ(English Title) |
Pulmonary Nodule Classification in Computed Tomography Image Using a 3D Deep Convolutional Neural Network |
ÀúÀÚ(Author) |
Á¤ÈÖÁø
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Hwejin Jung
Bumsoo Kim
Inyeop Lee
Junhyun Lee
Jaewoo Kang
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¿ø¹®¼ö·Ïó(Citation) |
VOL 24 NO. 12 PP. 0699 ~ 0702 (2018. 12) |
Çѱ۳»¿ë (Korean Abstract) |
Àü ¼¼°è ¾Ï ¹ßº´ÀÇ Å« ºñÁßÀ» Â÷ÁöÇÏ´Â Æó¾ÏÀ» Á¶±â¿¡ ¿¹¹æÇϱâ À§Çؼ´Â Æó °áÀýÀ» ã¾Æ³» ¾Ç¼º ¿©ºÎ¸¦ °Ë»çÇØ¾ß ÇÑ´Ù. º» ¿¬±¸¿¡¼´Â »ïÂ÷¿ø ½ÃÃþ Äܺ¼·ç¼Ç ½Å°æ¸ÁÀ» ÀÌ¿ëÇØ °áÀýÀÇ ¾Ç¼º ¿©ºÎ¸¦ ÆÇ´ÜÇÏ´Â ¸ðµ¨À» Á¦¾ÈÇÑ´Ù. ¼ôÄÆ ¿¬°áÀ» ÀÌ¿ëÇÑ ¸ðµ¨À» »ç¿ëÇß°í, ºÐ·ù ¼º´É Çâ»óÀ» À§ÇØ ¾Ó»óºí ±â¹ýÀ» ÀÌ¿ëÇÑ´Ù. º» ¸ðµ¨À» LUng Nodule Analysis 2016 ´ëȸ µ¥ÀÌÅÍ¿¡ Àû¿ëÇÏ¿© ¸ðµ¨ÀÇ ¼º´ÉÀ» ÃøÁ¤ÇÏ°í Á¤È®µµ¸¦ °ËÁõÇÑ´Ù. º» ¸ðµ¨Àº ´ëȸÀÇ Æò°¡ ÁöÇ¥ÀÎ Competition Performance Metric ±âÁØ 0.899¸¦ ±â·ÏÇÏ¿´°í, ÀÌ´Â ±âÁ¸ Âü°¡ÀÚµéÀÇ ¼º´É°ú ºñ±³ÇÏ¿´À» ¶§ ¿ì¼öÇÑ °á°úÀÌ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Early detection and examination of pulmonary nodules is the most effective ways to prevent lung cancer, accounting for more than a quarter of all cancer deaths. In this paper, we propose a 3D deep convolutional neural network for pulmonary nodule recognition. We use deep convolutional neural network that uses shortcut connections and the ensemble method is used to boost recognition performance. Proposed models are trained and tested on Lung Nodule Analysis 2016 competition dataset. We evaluate performance of models and verify preciseness. Proposed model produces 0.899 of Competition Performance Metric value, that is evaluation criteria of competition. It is outperforming value than that of other participants.
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Å°¿öµå(Keyword) |
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pulmonary nodule
lung cancer
deep learning
convolutional neural network
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