Deep Learning Techniques for Radiology Image Analysis on Scalable Cloud Platforms
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I4P104Keywords:
Deep learning, radiology, medical image analysis, cloud computing, convolutional neural networks, scalable platforms, medical diagnostics, healthcare AI, federated learning, AutoMLAbstract
Being one of the most important pillars in the field of medical diagnostics, radiology experienced a paradigm shift as a result of the introduction of Deep Learning (DL) and the growing use of cloud computing technologies. The increase in imaging data is exponential, particularly with modalities like X-rays, MRI, and CT, which require an effective and precise imaging analysis system. Traditional approaches are effective but often fail to work efficiently when large amounts of data are involved and the accuracy level is high. A sub-branch of machine learning, deep learning, uses neural networks to recreate human ability to make decisions and can be seen as a solution providing a paradigm shift in radiology image analysis. The use of deep learning models, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), has significantly improved radionics detection, segmentation, and classification procedures involved in medical imaging. However, these models cannot be run on ordinary computers, as they require substantial computational resources and, consequently, have necessitated the rise of large-scale cloud services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. These platforms provide high-performance GPUs, elastic compute resources and managed services, which enable DL model deployment as well as training at scale.
The article provides an impressive overview of the synergy between the cloud platform and DL methods in image analysis of radiology. We discuss the architecture of the famous DL models specific to radiology, use of transfer learning to utilize pre-trained models and the federated learning to maintain privacy of the data. A sober assessment of scalable cloud infrastructure is provided, which demonstrates the use of case studies in which DL models are used in a diagnostic task, such as the detection of COVID-19 using a chest X-ray and segmentation of brain tumours. The security and compliance, particularly regarding. HIPAA and GDPR are also reviewed; the importance of encrypted data transmission, secure storage, and audit controls within healthcare cloud settings is discussed. We point to performance, latency and cost trade-offs, and suggest a hybrid deployment as the best route to DL. As shown by our work, through the appropriate blend of DL architectures and scalable cloud infrastructure, it is possible to markedly increase the accuracy of diagnosis, decrease the workload of the radiologist, and open access to quality healthcare diagnostics to a much wider demographic, so-called top to bottom. And, at last, we cover new trends such as AutoML, the workings of edge clouds, and the future of quantum computing in the medical vision field
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