Optimized Deep Feature Selection for Pneumonia Detection: A Novel RegNet
  and XOR-Based PSO Approach
Fatemehsadat Ghanadi Ladani Samaneh Hosseini Semnani
Pneumonia remains a significant cause of child mortality, particularly in
developing countries where resources and expertise are limited. The automated
detection of Pneumonia can greatly assist in addressing this challenge. In this
research, an XOR based Particle Swarm Optimization (PSO) is proposed to select
deep features from the second last layer of a RegNet model, aiming to improve
the accuracy of the CNN model on Pneumonia detection. The proposed XOR PSO
algorithm offers simplicity by incorporating just one hyperparameter for
initialization, and each iteration requires minimal computation time. Moreover,
it achieves a balance between exploration and exploitation, leading to
convergence on a suitable solution. By extracting 163 features, an impressive
accuracy level of 98% was attained which demonstrates comparable accuracy to
previous PSO-based methods. The source code of the proposed method is available
in the GitHub repository.