An intelligent medical system using MRI to detect brain tumors utilizing enhanced computational efficiency and optimized segmentation

Ahed Abugabah, Zayed University

Document Type

Article

Source of Publication

Journal of Supercomputing

Publication Date

4-1-2025

Abstract

Detection of brain tumors should be both accurate and timely so that patient outcomes improve, and clinical intervention becomes feasible. This study develops a trustworthy machine learning pipeline for brain tumor identification using magnetic resonance imaging data. The pipeline's primary phases include preprocessing, segmentation, feature extraction, and classification. During the preprocessing phase, intensity standardization and noise removal are done via data normalization and cleaning to enhance image quality. Subsequently, an improved gray-level co-occurrence matrix and Fourier transform are done for more stable feature extraction, and an improved secretary sand cat optimization algorithm is done for more efficient feature selection and computation. These are then distinguished as optimized features by an ensemble model of deep belief networks, recurrent neural networks, and convolutional neural networks to identify tumor and non-tumor regions. Experimental results demonstrate the better accuracy, sensitivity, and specificity of the proposed method, and thus it is a trustworthy tool for automated brain tumor detection and diagnosis. Experimental validation confirms the putative pipeline's ability to achieve high accuracy, sensitivity, and specificity in the early detection and diagnosis of brain tumors.

ISSN

0920-8542

Publisher

Springer Science and Business Media LLC

Volume

81

Issue

5

Disciplines

Computer Sciences

Keywords

Brain tumor detection, Convolutional neural networks, Improved gray-level co-occurrence matrix, Improved secretary sand cat optimization, Magnetic resonance imaging, SegNet

Scopus ID

05002771173

Indexed in Scopus

yes

Open Access

no

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