ImageCLEF 2021: Deep categorizing tuberculosis cases using normalization and pseudo-color CT image

The ImageCLEF 2021 Tuberculosis task is an example of a challenging research problem in the field of computed tomography (CT) image analysis. The purpose of this study is to make accurate estimates for five labels (infiltrative, focal, tuberculoma, miliary, and fibrocavernous) based on lung images. We describe the tuberculosis task and approach for chest CT image analysis and then perform a single-label CT image analysis using the task dataset. We propose an image processing and fine-tuning deep neural network model that uses inputs from convolutional neural network features. This paper presents several approaches for applying normalization and pseudo-color to the extracted 2D images, for applying mask data to the extracted 2D image data, and for extracting a set of 2D projection images based on the 3D chest CT data. Our submissions for the task test dataset achieved an unweighted Cohen’s kappa of 0.117 and an accuracy of 0.382.