Enhanced Detection of Pulmonary Edema in Chest X-rays Using Deep Learning Ensembles with Attention Mechanism.

Abstract

Pulmonary edema, defined by the abnormal presence of excess fluid within the lungs, is a severe medical emergency that mandates accurate and immediate diagnosis. The use of classical diagnostic techniques-inspection, palpation, percussion, and auscultation-tends to be subjective and highly dependent on the clinician's experience, potentially resulting in variability in diagnosis and possible delays in treatment. This work provides a deep learning approach to the automatic diagnosis of pulmonary edema from chest X-ray images based on the NIH Chest X-ray dataset. The model based on the proposed CNN obtained a validation loss of 0.3350, an accuracy of 90%, and an F1-score of 0.91. The cross-validation further proved the model to be robust, with a total accuracy of 87%. These findings illustrate the performance of the model in the effective classification of pulmonary edema, hence facilitating quicker and more accurate clinical decision-making. Feature learning and representation were achieved with CNNs, boosted with attention and data augmentation strategies to favor generalization across patient populations and image variations. The integration of transparency aids like attention maps is imperative to validate the model's decision-making process, meeting the key criteria for clinical approval. In summary, this research provides a prospective solution to the early diagnosis of pulmonary edema, further leading to enhanced diagnostic processes and better patient care.