In many cases, accurate diagnoses of diseases rely heavily on image acquisition systems and image interpretation. Supervised learning requires large labeled datasets to learn and achieve good performance, and data augmentation, transfer learning, and dropout techniques have widely been employed in medical image processing to overcome the lack of such datasets.
Specifically, this survey explains the performance metrics of supervised learning methods summarizes the available medical datasets studies the state-of-the-art supervised learning architectures for medical imaging processing, including convolutional neural networks (CNNs) and their corresponding algorithms, region-based CNNs and their variants, fully convolutional networks (FCN) and U-Net architecture and discusses the trends and challenges in the application of supervised learning methods to medical image analysis. This survey aims to help researchers and practitioners of medical image analysis understand the key concepts and algorithms of supervised learning techniques. Deep learning-especially supervised deep learning-shows impressive performance in the classification, detection, and segmentation of medical images and has proven comparable in ability to humans.
PIXEL 3 F1 2019 IMAGES MANUAL
However, manual medical image analysis is tedious and time consuming, making it necessary to identify accurate automated methods.
Pathologists, radiologists, physicians, and researchers rely heavily on medical images to perform diagnoses and develop new treatments. Medical image interpretation is an essential task for the correct diagnosis of many diseases.
0 kommentar(er)
