Classification of tuberculosis with SURF spatial pyramid features

Abstract

Tuberculosis is threatening and hinders the socioeconomic development of countries burdened with TB cases. 75% of TB cases are documented in the productive age group of 15–54 years. The definitive diagnoses methods are timely expensive and lack sensitivity in recognizing all TB cases and in all stages. The development of CAD systems (Computer Aided Detection) will facilitate mass screening. In this work, we experimented the use of spatial pyramid of Speed-up Robust Features (SURF) in diagnosing TB. Though dense information representing the lung anatomy imply substantial generalization, the empirical results suggest otherwise. The SURF descriptors are extracted from a grid windows of several sizes and concatenated together. The SVM classifier with sigmoid kernel achieved AUC score of 89% in grid size of 64 pixels compared to only 73% in the concatenated spatial pyramid features.