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Texture-Based Evolutionary Method for Cancer Classification in Histopathology

Texture-Based Evolutionary Method for Cancer Classification in Histopathology

Kiran Fatima, Hammad Majeed
Copyright: © 2017 |Pages: 15
ISBN13: 9781522505716|ISBN10: 1522505717|EISBN13: 9781522505723
DOI: 10.4018/978-1-5225-0571-6.ch021
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MLA

Fatima, Kiran, and Hammad Majeed. "Texture-Based Evolutionary Method for Cancer Classification in Histopathology." Medical Imaging: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 558-572. https://doi.org/10.4018/978-1-5225-0571-6.ch021

APA

Fatima, K. & Majeed, H. (2017). Texture-Based Evolutionary Method for Cancer Classification in Histopathology. In I. Management Association (Ed.), Medical Imaging: Concepts, Methodologies, Tools, and Applications (pp. 558-572). IGI Global. https://doi.org/10.4018/978-1-5225-0571-6.ch021

Chicago

Fatima, Kiran, and Hammad Majeed. "Texture-Based Evolutionary Method for Cancer Classification in Histopathology." In Medical Imaging: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 558-572. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0571-6.ch021

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Abstract

Real-world histology tissue textures owing to non-homogeneous nature and unorganized spatial intensity variations are complex to analyze and classify. The major challenge in solving pathological problems is inherent complexity due to high intra-class variability and low inter-class variation in texture of histology samples. The development of computational methods to assists pathologists in characterization of these tissue samples would have great diagnostic and prognostic value. In this chapter, an optimized texture-based evolutionary framework is proposed to provide assistance to pathologists for classification of benign and pre-malignant tumors. The proposed framework investigates the imperative role of RGB color channels for discrimination of cancer grades or subtypes, explores higher-order statistical features at image-level, and implements an evolution-based optimization scheme for feature selection and classification. The highest classification accuracy of 99.06% is achieved on meningioma dataset and 90% on breast cancer dataset through Quadratic SVM classifier.

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