Resource-Efficient Attention-Aware Deep Learning for Pneumothorax Detection in Chest X-Ray Imaging

Abstract

This study develops a lightweight Convolutional Neural Network (CNN) model for pneumothorax detection in chest X-rays, targeting resource-limited healthcare environments. The proposed architecture integrates EfficientNetB2 for feature extraction, Long Short-Term Memory (LSTM) layers for sequential spatial modeling, and a Multi-Head Attention mechanism to enhance focus on critical regions. Trained on the SIIM-ACR Pneumothorax dataset (2,027 images), the model employs data augmentation, balancing, and preprocessing to address class imbalance and variability. The evaluation results demonstrate strong performance, achieving 86% accuracy, 93% recall, and a 0.91 AUC-ROC score, outperforming baseline models like ResNet-50 and MobileNet-v2. The model’s clinical applicability is further validated through Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations, highlighting lesion-specific regions, and a user-friendly GUI for real-world deployment. By optimizing computational efficiency while maintaining diagnostic accuracy, this work bridges the gap between deep learning and practical medical applications, particularly in underserved regions. Ethical, legal, and environmental considerations, including GDPR compliance and energy-efficient design, are systematically addressed to ensure responsible AI deployment.