한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)
- 한국정보처리학회 2011년도 춘계학술발표대회
- /
- Pages.1614-1615
- /
- 2011
- /
- 2005-0011(pISSN)
- /
- 2671-7298(eISSN)
DOI QR Code
Application of artificial neural network to differential diagnosis of lung lesion: Preliminary results
- Lee, Hae-Jun (Dept. of Nuclear Medicine, Gil Hospital) ;
- Lee, Yu-Kyung (Dept. of Nuclear Medicine, Gil Hospital) ;
- Hwang, Kyung-Hoon (Dept. of Nuclear Medicine, Gil Hospital)
- 발행 : 2011.04.30
초록
It is difficult to differentially diagnose between lung cancer and benign inflammatory lung lesion due to high false positive rate on F-18 FDG-PET. We investigated whether application of artificial neural network to this diagnosis may be helpful. We reviewed the medical records and F-18 FDG PET images of 12 patients, selecting clinical and PET variables such as SUV. For selected variables and confirm, multilayer neural perceptron was applied in crossvalidation method and compared to visual interpretation. Neural network correctly classified the lung lesions in 83%, and reduced greately the false positive rate. However, false negative rate was not influenced. Application of neural network to the differential diagnosis between lung cancer and benigh inflammatory lesion may be helpful. Further studies with more patients are warranted.
키워드