Abstract
Pneumothorax is a critical thoracic condition characterized by air accumulation in the pleural
cavity, which can be life-threatening if not promptly diagnosed and treated. However,
conventional chest radiograph diagnosis heavily relies on radiologists' experience, leading to
diagnostic variability and potential delays in treatment. This research proposes an innovative deep
learning model integrating EfficientNet and U-Net architectures to accurately detect
pneumothorax from chest X-ray images. The proposed hybrid EfficientNet-UNet model leverages
EfficientNet's robust feature extraction capabilities combined with U-Net’s effectiveness in
segmentation tasks, significantly enhancing detection precision. Comprehensive training and
validation were conducted using publicly available datasets of chest radiographs. Experimental
results demonstrated promising performance, with the model achieving a high ROC_AUC score
of 0.86, improved mean Intersection over Union (mIoU) of 0.20, and mean Dice coefficient
(mDice) of 0.30. Additionally, sensitivity reached approximately 0.40, while specificity remained
stable around 0.89, indicating the model’s robust discriminatory ability. These outcomes highlight
the proposed model’s effectiveness in automating pneumothorax diagnosis, offering a powerful
tool to assist clinical decision-making, reduce diagnostic errors, and improve patient outcomes.