Title: Texture-based approach to classification meningioma using pathology images

Authors: Yasmeen O. Sayaheen

Addresses: Yarmouk University, Irbid, Jordan

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).

Keywords: meningiomas tumour; texture feature; first order statistics; FOS; grey-level co-occurrence matrix; GLCM; grey-level run length statistic; GLRS; GLDS; neighbourhood grey-tone difference matrix; NGTDM; classification; support vector machine; SVM; k-nearest neighbours; KNN.

DOI: 10.1504/IJCVR.2023.134318

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.6, pp.677 - 692

Received: 12 Oct 2021
Accepted: 13 Jun 2022
Published online: 18 Oct 2023 *

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