Intelligent tuberculosis activity assessment system based on an ensemble of neural networks

Abstract

This article proposes a novel approach to assess the degree of activity of pulmonary tuberculosis by active tuberculoma foci. It includes the development of a new method for processing lung CT images using an ensemble of deep convolutional neural networks using such special algorithms: an optimized algorithm for preliminary segmentation and selection of informative scans, a new algorithm for refining segmented masks to improve the final accuracy, an efficient fuzzy inference system for more weighted activity assessment. The approach also includes the use of medical classification of disease activity based on densitometric measures of tuberculomas. The selection and markup of the training sample images were performed manually by qualified pulmonologists from a base of approximately 9,000 CT lung scans of patients who had been enrolled in the dispensary for 15 years.The first basic step of the proposed approach is the developed algorithm for preprocessing CT lung scans. It consists in segmentation of intrapulmonary regions, which contain vessels, bronchi, lung walls to detect complex cases of ingrown tuberculomas.To minimize computational cost, the proposed approach includes a new method for selecting informative lung scans, i.e., those that potentially contain tuberculomas.The main processing step is binary segmentation of tuberculomas, which is proposed to be performed optimally by a certain ensemble of neural networks. Optimization of the ensemble size and its composition is achieved by using an algorithm for calculating individual contributions. A modification of this algorithm using new effective heuristic metrics has been proposed which improves the performance of the algorithm for this problem.A special algorithm was developed for post-processing of tuberculoma masks obtained during the segmentation step. The goal of this step is to refine the calculated mask for the physical placement of the tuberculoma. The algorithm consists in cleaning the mask from noisy formations on the scan, as well as expanding the mask area to maximize the capture of the tuberculoma location area.A simplified fuzzy inference system was developed to provide a more accurate final calculation of the degree of disease activity, which reflects data from current medical studies.The accuracy of the system was also tested on a test sample of independent patients, showing more than 96% correct calculations of disease activity, confirming the effectiveness and feasibility of introducing the system into clinical practice.