AFCM-LSMA: New Intelligent Model based on Lévy Slime Mould Algorithm and Adaptive Fuzzy C-means for Identification of COVID-19 Infection from Chest X-ray Images

ABSTRACT

Problem: A worldwide challenge is to provide medical resources required for COVID-19 detection. They must be effective tools for fast detection and diagnose of the virus using a large number of tests;besides, they should be low-cost developments. While a chest x-ray scan is a powerful candidate tool, if several tests are carried out, the images produced by the devices must be interpreted accurately and rapidly. COVID-19 induces longitudinal pulmonary parenchymal ground-glass and consolidates pulmonary opacity, in some cases with rounded morphology and peripheral lung distribution, which is very difficult to predict in an early stage. Aim In this paper, we aim to develop a robust model to extract high-level features of COVID-19 from chest x-ray (CXR) images to help in rapid diagnosis. In specific, this paper proposes an optimization model for COVID-19 diagnosis based on adaptive Fuzzy C-means (AFCM) and improved Slime Mould Algorithm (SMA) based on Lévy distribution, namely AFCM-LSMA. Methods The SMA optimizer is proposed to adapt weights in oscillation mode and to mimic the process of generating positive and negative feedback from the propagation wave to shape the optimum path for food connectivity. Lévy motion is used as a permutation to perform a local search and to adapt SMA optimizer (LSMA) by generating several solutions that are apart from current candidates. Furthermore, it permits the optimizer to escape from local minima, examine large search areas and reach optimal solutions in fewer iterations with high convergence speed. The FCM algorithm is used to segment pulmonary regions from CXR images and is adapted to reduce time and amount of computations using histogram of the image intensities during the clustering process. Results The performance of the proposed AFCM-LSMA has been validated on CXR images and compared with different conventional machine learning and deep learning techniques, meta-heuristics methods, and different chaotic maps. The accuracies achieved by the proposed model are around (ACC=0.96, RMSE=0.23, Prec.=0.98, F1_score=0.98, MCC=0.79, and Kappa=0.79). Conclusion The experimental findings indicate that the proposed new method outperforms all other methods, which will be beneficial to the clinical practitioner for the early identification of infected COVID-19 patients.