Tuberculosis Detection in Chest Radiography: A Combined Approach of Local Binary Pattern Features and Monarch Butterfly Optimization Algorithm

Tuberculosis is a severe and contagious lung dis-ease that kills about 1.5 million people worldwide. One of the ways to combat this disease is by tracking, detecting, and iso-lating the infected. In this sense, chest radiography (CXR) is an effective alternative for this task, given its high availability, low charge, and quick response. Thus, considering the importance of this topic, our work proposal is a machine learning method for tuberculosis detection in CRXs. Our method combines local binary patterns (LBP) feature extraction and a feature selection wrapper algorithm by Monarch Butterfly Optimization (MBO) with an evaluation KNN classifier. The results are compared to a reference work on various metrics and show 90.33 % and 92.41 % accuracy and the area under the ROC curve, respectively. Our proposal is a solution that combines performance, reduced computational cost, and simplicity of implementation, composing a viable and aligned alternative to the Internet of Things (IoT) solutions.