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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) °èÃþÀû ½Å°æȸ·Î¸ÁÀ» ÀÌ¿ëÇÑ ÈĵÎÁúȯ °¨º° ºÐ·ù±â
¿µ¹®Á¦¸ñ(English Title) Implementation on the Classifier for Differential Diagnosis of Laryngeal Disease using Hierarchical Neural Network
ÀúÀÚ(Author) ±è°æÅ   ±è±æÁß   Àü°è·Ï   Kyung-Tae Kim   Gil-Jung Kim   Gye-Rok Jeon  
¿ø¹®¼ö·Ïó(Citation) VOL 06 NO. 01 PP. 0076 ~ 0082 (2002. 02)
Çѱ۳»¿ë
(Korean Abstract)
º» ¿¬±¸¿¡¼­´Â °èÃþÀû ½Å°æȸ·Î¸ÁÀ» »ç¿ëÇÏ¿© Á¤»ó, ÈĵÎÁúȯ(polyp, nodule, palsy µî), ÈĵÎÁúȯ Áß ¼º¹®¾Ï ½Ã±âº° °¨º°Áø´ÜÀÌ °¡´ÉÇÑ ÈĵÎÁúȯ °¨º°Áø´Ü ºÐ·ù±â¸¦ ±¸ÇöÇÏ¿´´Ù. ÈĵÎÁúȯÀ» °¡Áø ȯÀÚ±º°ú Á¤»ó±º, ±×¸®°í ¼º¹®¾ÏÀÇ °¢ ½Ã±âº°¿¡ ÇØ´çµÇ´Â ȯÀÚ±ºÀ¸·ÎºÎÅÍ /a/, /e/, /i/, /o/, /u/ ¸ðÀ½¿¡ µû¸¥ ºÐ·ùÀÛ¾÷À» ¼öÇàÇÏ¿´´Ù. °¢ ¸ðÀ½º° ºÐ·ù ½ÇÇèÀ» ¼öÇàÇÑ °á°ú ¸ðµç ÀÔ·Â ÆĶó¹ÌÅÍ¿¡ ´ëÇؼ­ /a/¸ðÀ½ÀÌ ´Ù¸¥ ¸ðÀ½¿¡ ºñÇØ ¿ì¼öÇÑ ºÐ·ùÀ²À» ³ªÅ¸³»¹Ç·Î, /a/¸ðÀ½¸¸À» »ç¿ëÇÏ¿© ÈĵÎÁúȯÀ» °¨º°Áø´ÜÇϱâ À§ÇÑ °èÃþÀû ½Å°æȸ·Î¸ÁÀ» ±¸ÇöÇÏ¿´´Ù. ±¸ÇöµÈ °èÃþÀû ½Å°æȸ·Î¸ÁÀº °¢ °èÃþº°·Î ¼­·Î ´Ù¸¥ ÆĶó¹ÌÅ͵éÀ» Àû¿ëÇÏ¿© ¿©·¯ ÈĵÎÁúȯÀ» °¨º°Áø´ÜÇϵµ·Ï ±¸¼ºµÇ¾ú´Ù.
¿µ¹®³»¿ë
(English Abstract)
In this paper, we implemented on the classifier for differential diagnosis of laryngeals disease which is normal, polyp, nodule, palsy, and each step of glottic cancer using hierarchical neural network. We conducted on classifier of various vowels as /a/, /e/, /i/, /o/, /u/ from normal group, laryngeal disease group, each step of cancer group. The experimental result on classification of each vowels as follows. A /a/ vowel shows excellent classification result to the other vowels in regard to each Input parameters. Thus we implemented the hierarchical neural network for differential diagnosis of laryngeals disease using only /a/ vowel. A implemented hierarchical neural network is composed of each other laryngeals disease apply to each other parameter in each hierarchical layer. We take the voice signals from patient who get the laryngeal disease and glottic cancer, and then use the APQ, PPQ, vAm, Jitter, Shimmer, RAP as input parameter of neural networks.
Å°¿öµå(Keyword) hierarchical neural network   glottic cancer   laryngeal disease  
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