Classification and Rule-Based Approach to Diagnose Pulmonary Tuberculosis

The pulmonary tuberculosis (TB) is diagnosed conventionally from the test results obtained from different medical examinations. The paper proposes a novel methodology using the classification technique called Identification tree (IDT) to diagnose TB computationally. The model reduces the number of parameters required for the diagnosis substantially. It also offers a list of rules for the speedy and easy diagnosis. The effectiveness of the method has been validated by comparing with existing techniques using standard detection measures.

[1]  Gabriel Cristobal,et al.  Automatic identification techniques of tuberculosis bacteria , 2003, SPIE Optics + Photonics.

[2]  Afrânio Lineu Kritski,et al.  Neural Networks: An Application for Predicting Smear Negative Pulmonary Tuberculosis , 2007 .

[3]  Fevzullah Temurtas,et al.  Tuberculosis Disease Diagnosis Using Artificial Neural Networks , 2010, Journal of Medical Systems.

[4]  A. El‐Solh,et al.  Predicting active pulmonary tuberculosis using an artificial neural network. , 1999, Chest.

[5]  K. N. Balasubramanya Murthy,et al.  Association-rule-based tuberculosis disease diagnosis , 2010, International Conference on Digital Image Processing.

[6]  Nejat Yumusak,et al.  Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm , 2011, Journal of Medical Systems.

[7]  Gabriel Cristóbal,et al.  Identification of tuberculosis bacteria based on shape and color , 2004, Real Time Imaging.

[8]  M F Beg,et al.  Image processing techniques for identifying Mycobacterium tuberculosis in Ziehl-Neelsen stains. , 2008, The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[9]  Afrânio Lineu Kritski,et al.  Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study , 2006, BMC public health.

[10]  Vishnu Vardhan Makkapati,et al.  Segmentation and classification of tuberculosis bacilli from ZN-stained sputum smear images , 2009, 2009 IEEE International Conference on Automation Science and Engineering.

[11]  C. Campbell,et al.  The Automated Identification of Tubercle Bacilli using Image Processing and Neural Computing Techniques , 1998 .

[12]  Christopher Dye,et al.  The growing burden of tuberculosis: global trends and interactions with the HIV epidemic. , 2003, Archives of internal medicine.

[13]  S. Pliska,et al.  Direct diagnosis of tuberculosis by computer assisted pattern recognition gas chromatographic analysis of sputum. , 1991, Biomedical chromatography : BMC.

[14]  Conrad Bessant,et al.  Prospects for Clinical Application of Electronic-Nose Technology to Early Detection of Mycobacterium tuberculosis in Culture and Sputum , 2006, Journal of Clinical Microbiology.