LAUSR

Tuberculosis is one of the most common infections in the human population, while reactivation of the MTB bacteria can lead to secondary pulmonary tuberculosis (SPT). SPT is a significant health risk for both immunocompromised individuals and the population of regional hotspots. This brief proposes a novel model for ensemble learning for the efficient identification of SPT in CT imaging. We reformulate the ensemble process as multiple instance learning and utilizes an attention-based pooling layer to ensemble features extracted from a range of base networks. The best performing model consists of 4 base models and an ensemble network with Hopfield-pooling. We abbreviate this model as Hopfield-pooling ensemble (HFPE). For the classification of CT slices between healthy controls and SPT patients, HFPE achieved an accuracy of 96.00±2.55%, specificity of 97.00±2.45%, precision of 96.99±2.46%, sensitivity of 95.00±4.47, and F1-score of 95.92±2.63%, achieving comparable or higher performances to a number of state-of-art approaches.