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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.<\/jats:p>","DOI":"10.1002\/jemt.22874","type":"journal-article","created":{"date-parts":[[2017,4,5]],"date-time":"2017-04-05T17:40:02Z","timestamp":1491414002000},"page":"851-861","source":"Crossref","is-referenced-by-count":8,"title":["Nuclear spatial and spectral features based evolutionary method for meningioma subtypes classification in histopathology"],"prefix":"10.1002","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3103-1419","authenticated-orcid":false,"given":"Kiran","family":"Fatima","sequence":"first","affiliation":[{"name":"Department of Computer Science National University of Computer and Emerging Sciences A. K. Brohi Road, H\u201011\/4, Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hammad","family":"Majeed","sequence":"additional","affiliation":[{"name":"Department of Computer Science National University of Computer and Emerging Sciences A. K. Brohi Road, H\u201011\/4, Islamabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Humayun","family":"Irshad","sequence":"additional","affiliation":[{"name":"Department of Pathology Beth Israel Deaconess Medical Center, Harvard Medical School Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,4,5]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00153759"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.01.005"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2014.05.013"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23623-5_83"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"Airf M. &Rajpoot N.(2007). Classification of potential nuclei in prostate histology images using shape manifold learning. 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