Bøger af Hubert Henson

Medical imaging or radiology is the field of medical science that involves the recreation of images of human body parts for the purpose of diagnosing and treating different medical conditions. There are various types of medical imaging procedures such as X-ray, magnetic resonance imaging (MRI), ultrasound, etc. The area of study within artificial intelligence which focuses on providing the computers with the capability to learn without being explicitly programmed is known as machine learning. Some radiological applications of machine learning include identification, portrayal and observation of infections, image classification, object detection, image segmentation and registration, image generation, and image transformation. Machine learning and artificial intelligence tools are used extensively for identifying cardiovascular abnormalities, neurological diseases, and detecting fractures and other musculoskeletal injuries. This book outlines the applications of machine learning in the field of medical imaging. It will prove to be immensely beneficial to medical students and researchers involved in this field.

Medical imaging refers to several different technologies that are used to view inside the human body in order to diagnose, monitor or treat medical conditions. Endoscopy, tactile imaging and computerized tomography (CT) scan are some common types of medical imaging procedures. In recent years, deep learning or deep structured imaging has found applications in the field of medical image analysis. Machine learning is a subfield of artificial intelligence (AI) and is used for various purposes such as computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. AI tools are used to accelerate productivity and potentially improve accuracy in the process of medical imaging. This book contains some path-breaking studies in the field of deep learning and medical imaging. The various studies that are constantly contributing towards advancing technologies and evolution of this field are examined in detail. It will serve as a valuable source of reference for medical students, radiologists, and industry researchers engaged in medical imaging research.