Optimizing Pneumonia Diagnosis Using RCGAN-CTL: A Strategy for Small or Limited Imaging Datasets

Abstract

In response to the urgent need for efficient pneumonia diagnosis—a significant health challenge that has been intensified during the COVID-19 era—this study introduces the RCGAN-CTL model. This innovative approach combines a coupled generative adversarial network (GAN) with relativistic and conditional discriminators to optimize performance in contexts with limited data resources. It significantly enhances the efficacy of small or incomplete datasets through the integration of synthetic images generated by an advanced RCGAN. Rigorous evaluations using a wide range of lung X-ray images validate the model’s effectiveness. In binary classification tasks that differentiate between normal and pneumonia cases, RCGAN-CTL demonstrates exceptional accuracy, exceeding 99%, with an area under the curve (AUC) of around 95%. Its capabilities extend to a complex triple classification task, accurately distinguishing between normal, viral pneumonia, and bacterial pneumonia, with precision scores of 89.9%, 95.5%, and 90.5%, respectively. A notable improvement in sensitivity further evidences the model’s robustness. Comprehensive validation underscores RCGAN-CTL’s superior accuracy and reliability in both binary and triple classification scenarios. This advancement is pivotal for enhancing deep learning applications in medical diagnostics, presenting a significant tool in addressing the challenges of pneumonia diagnosis, a key concern in contemporary healthcare.