Advancing Healthcare Anomaly Detection: Integrating GANs with Attention Mechanisms

Early illness diagnosis, treatment monitoring, and healthcare administration all depend heavily on the identification of abnormalities in medical data. This paper proposes a unique way to improve healthcare anomaly detection through the integration of attention mechanisms and Generative Adversarial Networks (GANs) for improved performance. By integrating GANs, artificial data that closely mimics the distributions of actual healthcare data may be produced, so, it is important to supplementing the dataset and strengthening the resilience of anomaly detection algorithms. Simultaneously, the Convolutional Block Attention Module (CBAM) facilitates the model's concentration on useful characteristics present in the data, thereby augmenting its capacity to identify minute deviations from the norm. The suggested method is assessed using a large dataset from healthcare settings that includes both typical and unusual cases. When compared to current techniques, the results show notable gains in anomaly detection performance. The model also shows resilience to noise, small abnormalities, and class imbalance, indicating its potential for practical clinical applications. The suggested strategy has the potential to improve clinical decision-making and patient care by giving doctors faster, more precise insights into anomalous health states. With an accuracy of around 99.12%, the suggested GAN-CBAM is implemented in Python software and outperforms other current techniques such as Gaussian Distribution Anomaly detection (GDA), Augmented Time Regularized (ATR-GAN), and Convolutional Long Short-Term Memory (ConvLSTM) by 2.97%. With potential benefits for bettering patient outcomes and the effectiveness of the healthcare system, the suggested strategy is a major step forward in the improvement of anomaly identification in the field of medicine.