Abstract—Lung adenocarcinoma is the most common cause of
lung cancer in Taiwan. However, it is difficult to detect lung
adenocarcinoma at early stage. Most radiologists usually decide
the treatment strategies by screening whether the patients have
the epidermal growth factor receptor (EGFR) gene mutations or
not. However, the screen is expensive and time-consuming.
Therefore, the aim of the present paper is to identify whether
there are EGFR mutations in tumors with CT image. We
segment the whole region of lung adenocarcinoma and extract
the texture features in spatial domain and frequency domain
using GLCM and Gabor Wavelet. Finally we use Neuro-fuzzy
based AdaBoost to classify and identify the relationship between
the texture features of lung adenocarcinoma CT images and
EGFR mutations. The experimental result in this paper shows
that our success rate was 92%, and we will improve our methods
to increase the recognition rate in the future.
Index Terms—Lung Adenocarcinoma, Epidermal growth
factor receptor, Gabor Wavelet, Neuro-fuzzy based AdaBoost
Ting-Chen Tsan and Chiun-Li Chin are with the Department of Medical
Informatics, Chung Shan Medical University, Taichung, Taiwan (e-mail:
tusbasa1@gmail.com, ernestli@csmu.edu.tw).
Hao-Hung Tsai is with the Department of Medical Imaging, Chung Shan
Medical University Hospital, Taichung, Taiwan (e-mail:
hao620620@gmail.com).
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Cite:Ting-Chen Tsan, Hao-Hung Tsai, and Chiun-Li Chin, "The Identification of EGFR Mutation Based on the CT Image of Lung Adenocarcinoma," International Journal of Information and Electronics Engineering vol. 6, no. 5, pp. 273-279, 2016.
