Deep Learning in Medical Imaging: Improving Accuracy through Advanced Data Analytics

Abstract

Deep learning has transformed medical imaging by transforming the automatic, precise, and efficient analysis of complex medical data. It improves conventional imaging modalities like X-ray, CT, MRI, and ultrasound with advanced neural networks, especially the convolutional neural networks. The most important methods are supervised, unsupervised, semi-supervised and transfer learning which are backed by efficient data preprocess and augmentation. Complex data analytics incorporates multimodal clinical data to enhance the diagnostics and treatment plans. Its uses are in disease detection, image segmentation, and computer-aided diagnosis. Although there are obstacles such as data privacy, interpretability, and small datasets, new technologies such as self-supervised learning and federated learning are making a further breakthrough in clinical healthcare systems.