THERMAL SCIENCE

International Scientific Journal

Faten K. Karim

,

Sara Ghorashi

,

Anis Ben Ishak

,

Azhari A. Elhag

,

Nahla Mohamed

INNOVATIVE MATHEMATICAL MODELLING APPROACHES TO DIAGNOSE CHRONIC NEUROLOGICAL DISORDERS WITH DEEP LEARNING

ABSTRACT
Multiple sclerosis impacts the central nervous system, causing symptoms like fatigue, pain, and motor impairments. Diagnosing multiple sclerosis often requires complex tests, and MRI analysis is critical for accuracy. Machine learning has emerged as a key tool in neurological disease diagnosis. This paper introduces the multiple sclerosis diagnosis network (MSDNet), a stacked ensemble of deep learning classifiers for multiple sclerosis detection. The MSDNet uses min-max normalization, the artificial hummingbird algorithm for feature selection, and a combination of LSTM, DNN, and CNN models. Hyperparameters are optimized using the enhanced walrus optimization algorithm. Experimental results show MSDNet's superior performance compared to recent methods.
KEYWORDS
chronic neurodegenerative disorder, advanced machine learning, refined walrus optimization method, bioinspired optimization technique
PAPER SUBMITTED: 2024-06-20
PAPER REVISED: 2024-09-05
PAPER ACCEPTED: 2024-09-27
PUBLISHED ONLINE: 2025-01-25
DOI REFERENCE: https://doi.org/10.2298/TSCI2406217K
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 6, PAGES [5217 - 5229]
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Innovative mathematical modelling approaches to diagnose chronic neurological disorders with deep learning

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence