Cancer recognition


CanDL: Cancer Recognition with Deep Learning

Dr David Harris-Birtill & Dr Roushanak Rahmat  | School of Computer Science

Detection of cancer in medical images, e.g. from PET, MRI or CT imaging systems, is one of the core components in the fight against cancer. Currently tumour detection and segmentation is a very labour intensive job which requires the time of a highly trained person (costly), and is not 100% accurate, with a variety of factors (including human error and fatigue) diminishing the accuracy of the method. By automating this process clinicians will be greatly aided in their role, helping them to detect cancerous regions with a greater level of accuracy, and increasing the speed of detection to allow faster diagnosis, both leading to a much improved patient outcome, and helping the workload of these trained professionals, as well as decreasing the cost of the overall diagnosis process for the healthcare provider. The goal of this project is to develop an automatic tumour detection and segmentation of many tumour types in various parts of human body using deep learning.