Automatic B cell lymphoma detection using flow cytometry data

Flow cytometry has been widely used for the diagnosis of various hematopoietic diseases. Although there have been advances in the number of markers that can be analyzed simultaneously, the data is still interpreted by manual gating. This is labor-intensive, time-consuming, and subject to human error. We propose a computational model to detect B-lymphocyte neoplasms using flow cytometry data by building healthy and sick profiles. A cell capture rate was defined to measure the fitness of a test subject using a particular profile. By examining the cell capture rate of a test case with all profiles, the disease type can be determined.