Inception-based Deep Learning Architecture for Tuberculosis Screening using Chest X-rays

The motivation for this work is the primary need of screening Tuberculosis (TB) positive patients in the severely resource constrained regions of the world. Chest X-ray (CXR) is considered to be a promising indicator for the onset of TB, but the lack of skilled radiologists in such resource constrained regions degrades the situation. Therefore, several computer aided diagnosis (CAD) systems have been proposed to solve the decision making problem, which includes hand-engineered feature extraction methods to deep learning or Convolutional Neural Network (CNN) based methods. Feature extraction, being a time and resource intensive process, often delays the process of mass screening. Hence, an end to end CNN architecture is proposed in this work to solve the problem. Two benchmark CXR datasets have been used in this work, collected from Shenzhen (China) and Montgomery County (USA), on which the proposed methodology achieved a maximum abnormality detection accuracy (ACC) of 91.7% (0.96 AUC) and 87.47% (0.92 AUC) respectively. Considering these datasets, to the best of our knowledge, the obtained results are superior to the state of the art deep learning based works.