New AI system shows promise for faster multiple sclerosis diagnosis
Researchers have developed an artificial intelligence system that can detect multiple sclerosis from MRI scans with greater accuracy than existing methods. The advance could accelerate diagnosis of a disease affecting millions globally, potentially reducing the years of uncertainty many patients currently face before treatment begins.
Originaltitel: Innovative mathematical modelling approaches to diagnose chronic neurological disorders with deep learning
<p>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.</p>