Pneumonia detection using butterfly optimization and hybrid butterfly optimization algorithm

Abstract

Pneumonia affects so many people around the world, which leads to many of them being killed. In order to identify and diagnose the disease, the patient must first undergo an x-ray scan of the chest (CXR). Disease will be identified according to the CXR images. Software diagnostic tools are used to help decision-making and to promote the pneumonia diagnosis process. From these tools is the gray level distribution moments (GLDM) algorithm which is used for CXR image features extraction, and we used the meta-heuristic algorithm representing the basic butterfly optimization algorithm using lèvy flight (BOALF) and the modified butterfly optimization algorithm (BOARN) to detect pneumonia on the basis of these extracted features. And then we've also been making hybrid between the BOA with fuzzy membership function to get as novel method called it fuzzy butterfly optimization algorithm (FBOA). These methods were based on various x-ray images of the chest. In testing phase, the proposed method obtained the highest diagnostic rate of the disease compared to the other two methods in this work.