NeuroXNet: Creating A Novel Deep Learning Architecture that Diagnoses Neurological Disorders, Finds New Blood Biomarkers, and Assesses Surgical, Drugs, and Radiation Treatment Plans Using Medical Imaging and Genomic Data

Neurodegenerative diseases and cancerous brain tumors cause millions of patients worldwide to be fatally ill and face cognitive impairment each year. Current diagnosis and treatment of these neurological conditions take many days, are sometimes inaccurate, and use invasive approaches that could endanger the patients life. Thus, this studys purpose is the creation of a novel deep learning model called NeuroXNet, which uses MRI images and genomic data to diagnose both neurodegenerative diseases like Alzheimers disease, Parkinsons disease, and Mild Cognitive Impairment as well as cancerous brain tumors, including glioma, meningioma, and pituitary tumors. Moreover, the model helps find novel blood biomarkers of differentially expressed genes to aid in diagnosing the six neurological conditions. Furthermore, the model uses patient genomic data to give additional recommendations for treatment plans that include various treatment approaches, including surgical, radiation, and drugs for higher patient survival for each class of the disease. The NeuroXNet model achieves a training accuracy of 99.70%, a validation accuracy of 100%, and a test accuracy of 94.71% in multi-class classification of the six diseases and normal patients. Thus, NeuroXNet reduces the chances of misdiagnosis, helps give the best treatment options, and does so in a time/cost-efficient manner. Moreover, NeuroXNet efficiently diagnoses diseases and recommends treatment plans based on patient data using relatively few parameters causing it to be more cost and time-efficient in providing non-invasive approaches to diagnosis and treatment for neurological disorders than current procedures.