Combining depthwise separable convolution and transfer learning for pneumonia classification

Abstract

Pneumonia represents a prevalent pulmonary illness within the general population, significantly impacting diverse demographic groups, most notably infants. Therefore, identifying the disease at an early stage is essential to manage the condition effectively and boost the likelihood of a complete recovery via proper therapeutic interventions. This research endeavors to construct a robust early diagnostic framework based on medical imaging of pneumonia. The methodology introduced evaluates the synergy of depthwise separable convolution, transfer learning, and soft voting mechanisms, where each predictive model undergoes rigorous optimization via particle swarm optimization (PSO). For this study, the dataset is strictly categorized into two distinct groups: normal and pneumonia-infected. The analytical findings demonstrate that the PSO-optimized soft voting ensemble achieves a peak accuracy rate of 92.11%. Such a substantial accuracy level suggests that the developed framework can function as a dependable supplementary diagnostic instrument in clinical environments, which could minimize human diagnostic errors and substantially reduce the time radiologists spend screening critical cases. Moreover, embedding these computational methodologies into interactive, user-friendly web dashboard framework highlights the model’s practical feasibility for delivering real-time diagnostic assistance in resource-constrained environments.