Automated Pneumonia Detection Using Manta Ray Optimizer with Deep Learning Approaches

This new method merging various disciplines into each stage, introduces a novel integration of state-of-the-art neural networks (MobileNet, ResNet, and VGG19) with an adaptive nature based on the Manta Ray scheme within the framework approaches helped by Adam optimizer to significantly improve accuracy across chest X-ray images. Integrating physics-motivated model parameter adaptation, based on the fluid-inspired lenticular lens architecture of manta rays (7), into MRO encourages more rapid convergence during training and allows for flexible manipulation in response to distributed behaviors inherent to medical imaging. This combined action not only accelerates convergence but also enhances the global stability of the model, improving classification accuracy and increasing reliability in diagnostics. Furthermore, the interoperability of these approaches highlights how cross-disciplinary research in healthcare technology can not only be transformational but also provide invaluable adjuncts to clinical practice for high-throughput targeted analyses. In an entirely new way, we want to transform traditional medical imaging analysis by bringing pervasively accurate diagnosis into everyday clinical practice while providing better outcomes for patients and leading "the cutting edge" of healthcare delivery. This ultimate workflow does seem to hold the key which can result in far-reaching improvements in health care, especially after making important progress upon the currently existing slower and less efficient healthcare systems. This approach is interdisciplinary and promises revolutionary progress in medical diagnostics.