New Deep Learning Models for Analyzing Largescale Medical Imaging Data with Precision
DOI:
https://doi.org/10.18486/ijcsnt/14.3.012Keywords:
Deep Learning, Medical Image Analysis, Big Data, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Healthcare, Largescale Datasets, Computational Efficiency, Data Privacy, Ethical Considerations, Regulatory ComplianceAbstract
Introduction of new deep learning technique has revolutionised the area of medical imaging, especially when it comes to handling of massive datasets that are a feature of healthcare systems. This paper investigates the application of state-of-the-art deep learning methods used for processing and interpretation of large-scale medical imaging data repositories. The main aim of this work is to tackle the difficulties arising due to high size, variety, and intricacy of these datasets by utilising deep neural network skills. The study also explores the infrastructure needs necessary to manage large-scale medical imaging datasets in a big data paradigm. It talks about how scalable computer frameworks and reliable data management systems work together to analyse, analyse, and extract useful information from massive volumes of imaging data. In the context of using deep learning to medical picture analysis, ethical issues, regulatory compliance, and data protection and security techniques are also discussed. In order to increase diagnostic accuracy and improve healthcare outcomes, this research intends to give a thorough understanding of the state of the art, difficulties encountered, and developments in applying cutting-edge deep learning techniques for analysing large-scale medical imaging data in a big data environment.
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