X-ray pneumonia detection using angular and radial local binary patterns fusion

Abstract

Detecting pneumonia via X-ray images is a crucial phase in determining any probable bacterial or viral disease. The automation of this stage is critical since it speeds up the diagnosing process. LBPs have excelled in investigating local conditions within a neighborhood, where they aggregate and analyze local statistical results. In this paper, we present a novel family of LBP descriptors based on local accumulative pixel disparities. We construct the new descriptors using a circular neighborhood acquired from the initial square filter. These descriptors consider angular and radial transitions that occur in both the microstructures and macrostructures of image textures. Eventually, a more accurate and comprehensive visual representation is obtained. Furthermore, we offer a data fusion step based on a voting mechanism to integrate the retrieved data efficiently. We show many types of analyses that demonstrate LBP's and the suggested extensions' capability to extract discriminant features from X-ray images. The suggested method is tested on two different datasets with large diversities that include images from various demographics and regions. Several measures, including accuracy rate, precision, sensitivity, F-measure, and specificity, are used to evaluate the system's efficacy. According to the testing results, the proposed system provides a best successful recognition rate of 82.7 %.