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

ABSTRACT

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 >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.

KEYWORDS:

• dementia
• ventricle
• grey wolf optimization
• dual deep learning techniques (DDLT)
• symmetry analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ahana Priyanka Nedunchellian

Ahana Priyanka Nedunchellian is currently a PhD student in Anna University. Her research interests include medical image processing (MIP) and soft computing techniques.

Kavitha Ganesan

Kavitha Ganesan is working as an Associate Professor in Anna University. She has more than 50 publication in reputed journals and conferences in the field of MIP.