Post-Traumatic Stress Disorder Diagnosis using Brain Cellular Resting-State Functional Magnetic Resonance Imaging with Stacked Deep Learning Framework

Background Post-traumatic stress disorder (PTSD) is caused by depression and stress affecting the brain's emotional, memory, and sensory processes. Materials and Methods This study investigates a stacked deep learning model for trauma-based PTSD disorder diagnosis using rs-fMRI scans. Twenty-eight individual subjects, fourteen PTSD, and fourteen healthy controls were used, and each subject had 140 Resting-State Functional MRI (rs-fMRI) scans. The selected subjects were assessed to obtain brain activation from twelve brain regions of interest. Results The boxplot was used to check the performance of twelve ROI brain regions. Different deep learning algorithms were used for classification through a 10-fold cross-validation approach. This study examines the efficacy of employing a stacked deep approach with two models in the realm of predictive modeling. Discussion The objective of the proposed tacking model is to enhance the overall prediction accuracy and durability by using the complementary attributes of each model. The stacked model achieved a 98.30% accuracy rate on the training dataset and 96.60% on the test dataset. Conclusion Using the proposed approach, we could detect PTSD at an early stage. The selected ROI regions could also discriminate healthy PTSD from infected regions due to trauma events such as violence, accidents, and terrorism.