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Ventricle pathology changes and their severity overlap among the dementia classes to understand the pathogenesis of this disorder. In the present work, left and right ventricle variations in the severity classes of dementia were observed using optimizations, along with the dual deep learning techniques (DDLT). Segmentation of ventricle region was carried out using a multilevel threshold-based grey wolf optimization and palindrome detection method was executed to identify its symmetry. AlexNet and ResNet were used to extract the DDLT features which are then used for classification. The obtained results showed that the ventricle region was accurately delineated with a higher degree of correspondence which was &gt;0.9. Furthermore, it was observed that the DDLT with multi-class SVM provided improved accuracy in the left ventricle with 84.8% and the right ventricle with 81.2%. Thus, the left ventricle variation was claimed to be a distinct indicator in demarcating different classes of dementia.

Study of onset in brain dementia using hierarchical wolf colony optimization and dual deep learning technique