title: Classification of follicular lymphoma images: a holistic approach with symbol-based machine learning methods.
abstract: It is not very often to see a symbol-based machine learning approach to be used for the purpose of image classification and recognition. In this paper we will present such an approach, which we first used on the follicular lymphoma images. Lymphoma is a broad term encompassing a variety of cancers of the lymphatic system. Lymphoma is differentiated by the type of cell that multiplies and how the cancer presents itself. It is very important to get an exact diagnosis regarding lymphoma and to determine the treatments that will be most effective for the patient's condition. Our work was focused on the identification of lymphomas by finding follicles in microscopy images provided by the Laboratory of Pathology in the University Hospital of Tenerife, Spain. We divided our work in two stages: in the first stage we did image pre-processing and feature extraction, and in the second stage we used different symbolic machine learning approaches for pixel classification. Symbolic machine learning approaches are often neglected when looking for image analysis tools. They are not only known for a very appropriate knowledge representation, but also claimed to lack computational power. The results we got are very promising and show that symbolic approaches can be successful in image analysis applications.
authors.affiliation: University of Maribor, Maribor, Slovenia. milan.zorman@uni-mb.si
authors.forename: Milan
authors.initials: M
authors.lastname: Zorman
authors.forename: José Luis
authors.initials: JL
authors.lastname: Sánchez de la Rosa
authors.forename: Dejan
authors.initials: D
authors.lastname: Dinevski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006694:Holistic Health; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008224:Lymphoma, Follicular / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted