Tuberculosis detection on chest X-rays using two-dimensional multiscale symbolic dynamic entropy

Abstract

Available online 14 July 2025International audienceSeveral radiological patterns associated with pulmonary tuberculosis (TB) have been identified on chest X-rays (CXR) used for screening purposes. As a result, several automatic computational tools have emerged for this purpose. We propose a new algorithm, two-dimensional multiscale symbolic dynamic entropy (MSDE), to develop a computational tool sensitive to these subtle patterns variations and noise robustness for evaluating CXR images from healthy and TB-diagnosed individuals. The one-dimensional SDE algorithm was previously shown to be more efficient in detecting amplitude variations and in computational calculations (compared to other entropy algorithms). Additionally, we also extracted first-order statistical parameters like standard deviation (SD), and mean of positive pixels (MPP), among others. These MSDE and first-order texture features were used to detect TB in each lung individually. The MSDE was validated using a synthetic dataset and optimized for the best set of parameters. We verified that, for both lungs, the MSDE values were significantly different between healthy and TB CXR images (), and the effect size was d 0.23. From the first-order parameters, only the mean, SD, entropy, and MPP were statistically different between both groups for the left lung (; d 0.22). For the right lung, all first-order features significantly differentiated TB patients (; d 0.28). Finally, we show that a multi-layer perceptron obtained 86.4 and 85.2% accuracy in detecting TB in the left and right lungs, respectively. The highest sensitivity values achieved in this study were 71.4% and 81.8% for the left and right lungs, respectively