Unveiling Lung Diseases in CT Scan Images With a Hybrid Bio-Inspired Mutated Spider-Monkey and Crow Search Algorithm

Abstract

Bio-inspired computer-aided diagnosis (CAD) has garnered significant attention in recent years due to the inherent advantages of bio-inspired evolutionary algorithms (EAs) in handling small datasets with elevated precision and reduced computational complexity. Traditional CAD models face limitations as they can only be developed post-outbreak, relying on datasets that become available during such events such as the COVID-19 pandemic. The scarcity of data for emerging diseases poses a substantial challenge to achieving elevated precision with conventional deep-learning algorithms. Furthermore, even when datasets are available, employing deep learning for class-based classification is arduous, necessitating model retraining, in this paper, we propose a novel hybrid algorithm that leverages the strengths of the crow search algorithm (CSA) and the spider monkey optimization (SMO) algorithm to create an optimised spider monkey crow search (OSM-CS) algorithm. We developed a CAD tool that maps each input CT image to a high-dimensional vector by extracting four categories of features: high contrast, polynomial decomposition, textural, and pixel statistics. The proposed OSM-CS algorithm is employed as a feature selection method. Our experimental results demonstrate the effectiveness of the OSM-CS algorithm, achieving an impressive accuracy of 98.2% when coupled with an AdaBoost classifier for multi-class classification and 99.93% for binary classification. This performance surpasses that of state-of-the-art (SOTA) deep learning models and recently published hybrid algorithms, underscoring the potential of the OSM-CS algorithm as a powerful tool in the realm of CAD.