Facial Recognition in Patient Monitoring Systems: Advanced Convolutional Neural Networks

Using sophisticated CNNs to improve healthcare delivery, facial recognition technology has emerged as a crucial instrument in patient monitoring systems. This technology has become more important in recent years. This solution makes use of powerful CNN architectures to reliably identify and verify patients based on facial characteristics. As a result, it enables patients to have access to their medical information and personalised treatment plans in a way that is both smooth and safe. These systems are able to monitor the health of patients, identify any abnormalities, and offer early warnings for medical actions because they integrate high-resolution imaging with real-time processing. Because the use of such sophisticated CNNs guarantees a high level of accuracy and resilience in a variety of clinical settings, face recognition has become an extremely significant asset in the domain of contemporary healthcare technology. The hybrid model, which combines ResNet and DenseNet, outperforms both architectures in deep learning tasks, especially in medical diagnostics. It improves feature learning, stable training, and gradient flow by combining ResNet's residual connections with DenseNet's dense connections. This approach addresses individual model limitations, reducing overfitting and improving generalization. The hybrid model shows higher accuracy, sensitivity, and specificity compared to ResNet and DenseNet alone. Radar charts demonstrate its performance across various medical datasets, demonstrating its potential for handling complex medical data.