TY - JOUR
TI - Innovative mathematical modelling approaches to diagnose chronic neurological disorders with deep learning
AU - Karim Faten K
AU - Ghorashi Sara
AU - Ishak Anis Ben
AU - Elhag Azhari A
AU - Mohamed Nahla
JN - Thermal Science
PY - 2024
VL - 28
IS - 6
SP - 5217
EP - 5229
PT - Article
AB - 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.