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.