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Schizophrenia Diagnosis using Signal Processing and Optimized Federated Learning Model


Mustafa Abdul Salam, Elsayed Badr, Eman Monier, and Alwan Mohamed


Vol. 22  No. 4  pp. 829-838


With the privacy concern of mental patients’ records as it is protected with federal privacy legislation, this paper proposes an optimized federated learning model for schizophrenia detection from functional magnetic resonance imaging fMRI and structural magnetic resonance imaging sMRI outputs. As the diagnosis of schizophrenia has no biological indicator, this study investigated the human brain's functional and structural defense for the disorder. fMRI and sMRI have an effective contribution to the diagnosis of schizophrenia and differentiate it from a healthy control. The proposed models predict schizophrenic states among MLSP 10th data. To improve the classification of traditional models on magnetic resonance data, a meta-heuristic model is proposed to improve the classification accuracy. Subsequently, the swarm-selected features are proved as most influential were used for machine learning algorithm building and evaluation of six models. There was a great achievement of increasing the performance. The proposed federated learning model and hybrid K-nearest neighbors model reached 100% of accuracy, and area under curve metrics.


Federated Learning, Schizophrenia, fMRI, sMRI, Swarm Intelligence.