Two-Stage Deep Feature Selection Method Using Voting Differential Evolution Algorithm for Pneumonia Detection From Chest X-Ray Images

Chest X-ray images play a crucial role in pneumonia diagnosis, with deep transfer learning being a widely adopted method for pneumonia detection. However, effectively handling feature data extracted from deep models without succumbing to the challenges of feature dimensionality remains a formidable task. In response to this complex issue, we propose a novel two-stage deep feature selection (FS) method utilizing the voting differential evolution (VDE) algorithm. In this approach, a dimension adaptive search strategy is meticulously devised to ensure robust feature selection while concurrently reducing the dimension. To expedite the optimization process, we devise a CR adaptive adjustment method to enhance the efficiency of the algorithm. Notably, an important aspect of our approach is the introduction of a novel DE algorithm that integrates a voting mechanism. This synergistic fusion allows a comprehensive analysis of crucial feature relationships to mitigate the risk of algorithmic entrapment in local optima. Additionally, we propose a dynamic feature evaluation function to avert the oversight of feature sets with optimal classification accuracy during later stages of the algorithm, thereby preserving discriminative features. The method is verified on an open Chest X-Ray Images dataset, achieving 99.04% average precision, 98.67% average accuracy, 99.13% average recall, and 19.93% average feature dimension reduction ratio. The experimental findings reveal that the presented method outperform prevailing state-of-the-art algorithms.