Enhancing Tuberculosis Detection: A Review of Optimization Algorithms in Medical Imaging

Abstract

Tuberculosis (TB) remains a major global health challenge, causing approximately 1.5 million deaths annually and affecting over 10.6 million people worldwide as of 2021. In countries like South Africa, TB remains a leading cause of mortality. Caused by Mycobacterium tuberculosis, the disease primarily affects the lungs but can spread to other organs. Early and accurate diagnosis is crucial to reduce transmission and mortality rates. This review focuses on the role of optimization algorithms in enhancing machine learning (ML) and deep learning (DL) models for TB detection in medical imaging. It explores chest X-rays (CXR) images as the main diagnostic imaging data, while emphasizing the use of these optimization algorithms for image segmentation, feature selection, and hyperparameter tuning. This study evaluates the performance of seven optimization algorithms in improving TB detection accuracy: genetic algorithm (GA), surrogate algorithm, particle swarm optimization (PSO), pattern search (PS), particle swarm optimization with pattern search (PSOPS), genetic algorithm with pattern search (GAPS), and firefly algorithm. The algorithms were  implemented using data from chest X-ray images. The results indicate that the top three performing algorithms are PSOPS (accuracy 78%, recall 80%, and specificity 78%), surrogate (accuracy 72%, recall 86%, and specificity 68%), and GAPS (accuracy 62%, recall 91%, and specificity 55%), based on comparisons with the ground truth image. The experiments and review in this study offer valuable insights for researchers and practitioners while identifying opportunities for future research. These insights can guide practitioners in choosing suitable optimization algorithms for TB detection, improving accuracy, efficiency, and scalability. Such improvements could enhance diagnostics, enabling early detection and intervention and thereby reducing the global TB burden.