Revisiting Channel and Spatial Attention in ResNet50 for Chest X-ray Classification

Abstract

Attention mechanisms are widely adopted in convolutional neural networks to improve feature representation, yet their consistent effectiveness in medical image classification remains uncertain. This study systematically evaluates channel and spatial attention mechanisms integrated into a ResNet50 architecture for chest X-ray classification tasks. Three attention configurations are examined, namely Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and a hybrid combination of both, using three publicly available chest X-ray datasets: Pneumonia Chest X-ray, COVID-QU-Ex, and Tuberculosis Chest X-ray. All model variants are trained under identical preprocessing and optimization settings to ensure a controlled and fair comparison. Each experiment is repeated across multiple random seeds to reduce the influence of stochastic variability, and final performance is reported as the mean across runs. Model effectiveness is assessed using accuracy, precision, recall, and F1 score, followed by paired t-tests to determine the statistical significance of observed differences. Quantitative results indicate that attention-augmented models produce marginal performance changes relative to the ResNet50 baseline across all three datasets. Statistical testing confirms that none of the observed improvements reach significance, with all p-values exceeding 0.05. These findings suggest that attention mechanisms do not function as universally effective performance boosters in chest X-ray classification. Instead, their role appears to be supportive in nature, contributing to feature representation stability rather than driving substantial predictive gains. Dataset characteristics and attention placement are identified as key factors influencing the degree of benefit observed.