Study of Dementia Severity Prognosis in Mr Images Using Grey Wolf Optimization Based Dual Deep Learning Technique

Prognosis of in a dementia disorder is a tedious task in preclinical stage. Ventricle pathology changes in dementia appear to be overlapped for neuro degeneration in brain. Identification of these overlaps among the groups severity helps to understand the pathogenesis of this disorder. In this work impact of changes in ventricle region on severity stages of dementia is observed using dual deep learning techniques (DDLT). Alzheimer's Disease Neuroimaging Initiative (ADNI) database that contains 1169 MR images are used in this study. Segmentation of ventricle region is carried out using multilevel threshold based Grey Wolf Optimization (GWO) technique. The feature vectors obtained from combined AlexNet and ResNet are analysed. The fused feature vectors are given to support vector machine (SVM) to observe the severity changes. Consequently, symmetry analysis of ventricle is carried out to perceive the distinctive changes in progression. The obtained results show that ventricle region is accurately delineated from other region with optimized thresholds. The segmented ventricle shows better correlation for all considered classes (> 0.9). It is observed that DDLT with multiclass SVM provides an improved accuracy of about 79.87% compared to individual transfer learning such as AlexNet (74%) and ResNet (76.53%). Further, symmetry analysis shows that left side ventricle with DDLT features shows an improved performance than right side for onset stages. Further, clinical correlation of left ventricle seems to be statically significant (p