Abstract
Visual computing is a discipline in computer science that allows computers to acquisite, process, and analyze visual data. In the application field of medicine, it enables to development of new methods for the visualization, detection, and analysis of diseases. Therefore, it plays a significant role in developing the understanding of diseases. Taking the fact that cancer is currently one of the deadliest and most frequent diseases around the world into consideration, it is crucial to generate effective methods for the study of cancer in order to provide appropriate management strategies for the disease and treatment methods. Research has shown that methods based on visual computing can detect, classify, and analyze the cancerous tissues in a patient successfully with high accuracy. This paper focuses on the PET/CT, CAD, CNN, and ST-Net visual computing models by evaluating their working mechanisms, their efficacy in the use of cancer with the help of previous research made in the literature, and their limitations in a medical approach.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
