Open AccessDense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation
Author(s) -
Roohi Sille,
Tanupriya Choudhury,
Piyush Chauhan,
Durgansh Sharma
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350306
Subject(s) - segmentation , artificial intelligence , computer science , pattern recognition (psychology) , scale space segmentation , sørensen–dice coefficient , cluster analysis , dice , market segmentation , hierarchical clustering , segmentation based object categorization , noise (video) , image segmentation , image (mathematics) , mathematics , statistics , marketing , business
Brain tumor segmentation is an essential and challenging task because of the heterogeneous nature of neoplastic tissue in spatial and imaging techniques. Manual segmentation of the tumor in MRI images is prone to error and time-consuming tasks. An efficient segmentation mechanism is vital to the accurate classification and segmentation of tumorous cells. This study presents an efficient hierarchical clustering-based dense CNN approach for accurately classifying and segmenting the brain tumor cells in MRI images. The research focuses on improving the efficiency of the segmentation algorithms by considering the qualitative measures such as the dice score coefficient using quantitative parameters such as mean square error and peak signal to noise ratio. The experimental analysis states the efficacy and prominence of the proposed technique compared to other models are tabulated within the paper.