ImageCLEF 2020: Deep Learning for Tuberculosis in Chest CT Image Analysis based on multi-axis projections

ImageCLEF 2020 Tuberculosis Task is an example of the challenging research problem in the field of CT image analysis. The purpose of this research is to make accurate estimates for the three labels (affected, pleurisy, caverns) for each of the lungs. We describe the tuberculosis task and approach for chest CT image analysis, then perform multi-label CT image analysis using the task dataset. We propose finetuning deep neural network model that uses inputs from multiple CNN features. In addition, this paper presents two approaches for applying mask data to the extracted 2D image data and for extracting a set of 2D projection images along multi-axis based on the 3D chest CT data. Our submissions on the task test dataset reached a mean AUC value of about 75% and a minimum AUC value of about 69%