TY - JOUR
T1 - Lung Tumor Classification on Human Chest X-Ray Using Statistical Modelling Approach
AU - Rizka, N.
AU - Chamidah, N.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/7/1
Y1 - 2019/7/1
N2 - Lung tumor is a group of abnormal cells that are formed from the process of excessive and uncoordinated cell division in the lung or known as a neoplasia. Neoplasia refers to the growth of new cells that are different from the growth of cells around it. The Tumor can formed to be benign tumors that not cause cancer and malignant tumors that can cause cancer. Chest X-ray is the most technique that used for detecting a lung tumor. Image processing is done by mean for distinguishing the classification lung tumor. Based on previous research the most used method is the mathematical method, but the result obtained are not maximal. Therefore, in this study we propose methods to classify lung tumor by using statistical modelling approach with logit link function based on parametric model, and nonparametric model using penalized spline estimator. Based on the proposed method, we get the classification accuracy of 80% for parametric model approach and 85% for nonparametric model approach, it means that the nonparametric model approach is better than the parametric model approach.
AB - Lung tumor is a group of abnormal cells that are formed from the process of excessive and uncoordinated cell division in the lung or known as a neoplasia. Neoplasia refers to the growth of new cells that are different from the growth of cells around it. The Tumor can formed to be benign tumors that not cause cancer and malignant tumors that can cause cancer. Chest X-ray is the most technique that used for detecting a lung tumor. Image processing is done by mean for distinguishing the classification lung tumor. Based on previous research the most used method is the mathematical method, but the result obtained are not maximal. Therefore, in this study we propose methods to classify lung tumor by using statistical modelling approach with logit link function based on parametric model, and nonparametric model using penalized spline estimator. Based on the proposed method, we get the classification accuracy of 80% for parametric model approach and 85% for nonparametric model approach, it means that the nonparametric model approach is better than the parametric model approach.
UR - http://www.scopus.com/inward/record.url?scp=85069446459&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/546/5/052065
DO - 10.1088/1757-899X/546/5/052065
M3 - Conference article
AN - SCOPUS:85069446459
SN - 1757-8981
VL - 546
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 5
M1 - 052065
T2 - 9th Annual Basic Science International Conference 2019, BaSIC 2019
Y2 - 20 March 2019 through 21 March 2019
ER -