Title: Brain tumour detection systems based on histopathological image analysis using segmentation and classification by deep learning architectures

Authors: Abdullah O. Alamoudi

Addresses: Department of Radiological Sciences and Medical Imaging, Majmaah University, 15341, Kingdom of Saudi Arabia

Abstract: The human brain has billions of cells and is one of the body's most complicated organs. This research proposes a novel technique in histopathological image analysis for detecting brain tumour by segmentation with classification utilising deep learning (DL) methods. Input images have been taken as histopathological images and processed for noise removal, smoothening, and normalisation using adaptive median filtering and MachenoStain normalisation. Then the processed image was segmented utilising an active contour-based Kernel k-means clustering operation where the tumour region has been segmented, and this segmented part has been classified. Boltzmann Q-learning with a convolutional network classified the tumour region and analysed its volume. Experimental analysis is carried out for various histopathological brain images for the proposed technique compared to the existing technique. The parameters compared are accuracy of 96%, sensitivity of 91%, specificity of 86%, coefficient of dice of 85%, Jaccard's coefficient of 96%, spatial overlap of 68%, AVME of 53%, and FoM of 63%.

Keywords: brain tumour; segmentation; classification; histopathological image analysis; deep learning.

DOI: 10.1504/IJSSE.2024.140747

International Journal of System of Systems Engineering, 2024 Vol.14 No.5, pp.539 - 563

Received: 23 Feb 2023
Accepted: 23 Mar 2023
Published online: 02 Sep 2024 *

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