Texture-based approach to classification meningioma using pathology images

Abstract

Manual analysis and judgement system suffered by two boundaries: first, studying histological slides by manual human's effort is time overhead and the human specialists are not permanently obtainable. Secondly, a lot of work has been done to outline diagnostic standards for all tumour components. CAD is quickly developing owing to the obtainability of up-to-date computing procedures, fresh imaging tools, plus patient data for infection diagnosis. Decision making using computer-assisted can be used to enhance histopathologists by providing additional objective diagnostic and analytic parameters. Recently, tumour has become one of the diseases that affect human health the most. Brain is a central system for human bodies that control, organise and arrange regular habit tasks. This paper talks about meningiomas tumour, which is considered one of the popular brain tumours. Colour-based segmentation, is a morphological operation used to enhance the appearance of cells. Texture-based features (FOS, GLCM, GLRS, GLDS and NGTDM) are used to enhance CAD feature extraction process and two classifiers are used to improve decision making (SVM and KNN).