An Intelligent Hybrid Optimization with Deep Learning model-based Schizophrenia Identification from Structural MRI

Authors

Raed N. Alabdali, Department of Computer Science, College of Science and Arts in Qurayyat, Jouf University, Sakakah, Saudi ArabiaFollow

Abstract

One of the fatal diseases that claim women while they are pregnant or nursing is schizophrenia. Despite several developments and symptoms, it can be challenging to discern between benign and malignant conditions. The main and most popular imaging method to predict Schizophrenia is MR Images. Furthermore, a few earlier models had a definite accuracy when diagnosing the condition. Stable MRI criteria must also be implemented immediately. Compared to other imaging technologies, the MRI imaging method is the simplest, safest, and most common for predicting Schizophrenia. The following factors are mostly involved in the subprocess for the initial MRI image. Before calculating the length between the sample point and the cluster center, the initial cluster center of the sample is identified. Classification is done according to how far the sample point is from the cluster center. The picture is then generated once the new cluster center has been derived using the classification history and verified to match the cluster convergence condition. A grey wolf optimization-based convolutional neural network approach is offered to get beyond the limitations and find schizophrenia, whether its hazardous or not. Many MRI images or datasets are analyzed in a short time, and the results show a more accurate or higher rate of schizophrenia recognition.

Recommended Citation

N. Alabdali, Raed (2023) "An Intelligent Hybrid Optimization with Deep Learning model-based Schizophrenia Identification from Structural MRI," Information Sciences Letters: Vol. 12 : Iss. 3 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol12/iss3/53