title: Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology.
abstract: Meningioma subtypes classification is a real-world multiclass problem from the realm of neuropathology. The major challenge in solving this problem is the inherent complexity due to high intra-class variability and low inter-class variation in tissue samples. The development of computational methods to assist pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this article, we proposed an optimized evolutionary framework for the classification of benign meningioma into four subtypes. This framework investigates the imperative role of RGB color channels for discrimination of tumor subtypes and compute structural, statistical and spectral phenotypes. An evolutionary technique, Genetic Algorithm, in combination with Support Vector Machine is applied to tune classifier parameters and to select the best possible combination of extracted phenotypes that improved the classification accuracy (94.88%) on meningioma histology dataset, provided by the Institute of Neuropathology, Bielefeld. These statistics show that computational framework can robustly discriminate four subtypes of benign meningioma and may aid pathologists in the diagnosis and classification of these lesions.
authors.affiliation: Department of Computer Science, National University of Computer and Emerging Sciences, A. K. Brohi Road, H-11/4, Islamabad, Pakistan.
authors.forename: Kiran
authors.identifier: http://orcid.org/0000-0003-3103-1419
authors.initials: K
authors.lastname: Fatima
authors.affiliation: Department of Computer Science, National University of Computer and Emerging Sciences, A. K. Brohi Road, H-11/4, Islamabad, Pakistan.
authors.forename: Hammad
authors.initials: H
authors.lastname: Majeed
authors.affiliation: Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
authors.forename: Humayun
authors.initials: H
authors.lastname: Irshad
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000378:metabolism*; D006652:Histological Techniques / Q000379:methods*; D006653:Histology; D006801:Humans; D008577:Meningeal Neoplasms / Q000378:metabolism* / Q000473:pathology*; D008579:Meningioma / Q000235:genetics / Q000378:metabolism* / Q000473:pathology*; D010336:Pathology / Q000379:methods*; D060388:Support Vector Machine
keywords: brain tumor classification; cell segmentation; meningioma; spectral analysis; texture analysis