Deep Learning Approaches for Medical Image Processing in the Big Data Era

Abstract

Diagnostics, therapy planning, and patient monitoring are areas where medical image processing has become indispensable in modern healthcare. The advent of deep learning techniques and the broad usage of big data in healthcare have brought a revolutionary shift in medical picture analysis and interpretation. An overview of deep learning techniques for medical image processing based on big data is given in this article. This article covers the pros and cons of adopting big data for medical imaging, from data storage and analysis to data capture. Beyond that, we take a look at medical image analysis using deep learning algorithms such as recurrent neural network (RNN), Convolutional Neural Network (CNN), and generative adversarial network (GAN), and we highlight its advantages and disadvantages. We also examine recent innovations such as transfer learning, multi-modal imaging fusion, and federated learning, which can improve the accuracy and efficiency of medical image processing systems. Finally, we discuss how medical image processing driven by deep learning could improve clinical decision-making, patient outcomes, and the development of personalized medicine in the era of data-driven healthcare.