Deep Learning Algorithms For Enhancing Image Processing In Healthcare Applications

Abstract

Deep learning algorithms have achieved significant progress in picture identification during recent years which coincides with the growing digitisation of electronic health records (EHRs) and diagnostic imaging. This investigation examines deep learning methods applied to medical image processing with an emphasis on Convolutional Neural Networks (CNNs) and their clinical importance. A comprehensive examination of critical medical imaging challenges such as classification detection segmentation localisation and registration is presented. The study showcases CNNs, RNNs, Autoencoders, and GANs as advanced architectures in both supervised and unsupervised learning categories. We also conduct an examination of existing benchmarks alongside performance indicators while evaluating available public data. Lastly, we tackle some of the pressing challenges like data scarcity, class imbalance, and how to interpret models, and we offer suggestions for future research and practical application in real-world healthcare.

Ähnliche Arbeiten