Modern Deep Learning Techniques for Big Medical Data Processing in Cloud

The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. Therefore, in this paper, an examination of the characteristics and challenges of big data is conducted with, highlighting the need for sophisticated analytical approaches. The paper then explores a wide spectrum of ML techniques, including unsupervised, supervised, and reinforcement learning methods, and their applications in big data contexts. This study explores recent advancement in DL architectures, while examining their effectiveness. Furthermore, we will show main challenges and limitations associated with using ML and DL in big data settings, including computational complexity, issues of data quality, model interpretability, and ethical concerns. Finally, it concludes by discussing the trends and future directions that are upcoming, including federated learning, explainable AI, and edge computing. Additionally, an experimental case study is presented to validate our approach, demonstrating its effectiveness in real-world healthcare scenarios through improved diagnostic accuracy and reduced computational time. Results have shown that the incorporation of DL techniques, such as CNN and RNN, forms the basis for large-scale analytics in medical data analytics due to their great contribution toward the development of accuracy and scalability. In specific research on disease prediction and medical image diagnosis within the healthcare industry, there was an improvement in diagnostics by up to 20%, with a reduction in computation time achieved via cloud-based architecture. These developments demonstrate the high extent of integration between DL and cloud computing in handling the challenge of bulk medical datasets and providing practical insight for both researchers and health professionals.