TY - GEN
T1 - Tuberculosis bacteria detection based on Random Forest using fluorescent images
AU - Zheng, Chi
AU - Liu, Jingxin
AU - Qiu, Guoping
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/13
Y1 - 2017/2/13
N2 - Tuberculosis (TB) is an infectious disease in low and middle-income countries. There are many methods of physical examinations for tuberculosis detection, but the most effective method is visual examination using microscopes, including fluorescent microscopy and bright field microscopy. However, according to the analysis of previous research work, the method based on fluorescent microscopes can yield on average 10% on sensitiveness than the bright field microscopy. In this paper, we present a TB detection method based on Random Forest using fluorescent microscopic images. We have conducted experiments on three types of classifiers, in terms of Random Forest (RF), linear SVM (LinSVM), and Cross-Validation SVM (CVSVM). The experimental results show that the machine learning method of Random Forest for TB segmentation and detection using fluorescent images has obtained better performance than other two methods.
AB - Tuberculosis (TB) is an infectious disease in low and middle-income countries. There are many methods of physical examinations for tuberculosis detection, but the most effective method is visual examination using microscopes, including fluorescent microscopy and bright field microscopy. However, according to the analysis of previous research work, the method based on fluorescent microscopes can yield on average 10% on sensitiveness than the bright field microscopy. In this paper, we present a TB detection method based on Random Forest using fluorescent microscopic images. We have conducted experiments on three types of classifiers, in terms of Random Forest (RF), linear SVM (LinSVM), and Cross-Validation SVM (CVSVM). The experimental results show that the machine learning method of Random Forest for TB segmentation and detection using fluorescent images has obtained better performance than other two methods.
KW - Fluorescent microscopy
KW - Image processing
KW - Random Forest
KW - Tuberculosis bacteria
UR - http://www.scopus.com/inward/record.url?scp=85016000563&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI.2016.7852772
DO - 10.1109/CISP-BMEI.2016.7852772
M3 - Conference Proceeding
AN - SCOPUS:85016000563
T3 - Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
SP - 553
EP - 558
BT - Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016
Y2 - 15 October 2016 through 17 October 2016
ER -
