Multi-scale cross spectral coherence and phase spectral distribution based measurement in non-subsampled shearlet domain for classification of brain tumors

Abstract

Computerized assessment of brain tumor identification and discrimination process from magnetic resonance imaging is of immense concern for improved investigation, growth rate prediction, treatment planning and monitoring of clinical trials. Due to extensive diversification of tumors and complex outline interactions between sub-regions, it is of a great challenge to extract significant features. The proposed approach starts with Grey Wolf Optimization based Gabor Filtering for enhancement process and spatial fuzzy c-means clustering algorithm for segmentation purpose. Following this, we apply non-sub-sampled shearlet transformation to investigate multi-scale space-frequency representation of tumor data. In order to measure the hierarchical correlation pattern of tumors, interlayer cross spectral coherence function is analyzed in terms of some significant indices such as fundamental global mean, secondary mean pair, span of secondary mean pair, percentage of samples having values greater than upper secondary mean pair and percentage of samples having values smaller than lower secondary mean pair. Additionally, topological profile of tumor getsassessed from its phase spectrum distribution pattern. Regarding this, some phase spectrum distribution-based quantifiers such as phase-sign change counter, phase change frequency, figure of merit and standard deviation of phase sign change location are introduced as the significant indices. These feature quantifiers have been fed to a deep neural network model and hybrid AdaBoost classifier to discriminate the state of benignancy/malignancy of tumors. Experimental results demonstrate that the overall accuracy attained by the proposed model are 99.67 %, 98.92 %, 96.89 %, 97.82 %, 96.98 %, 99.54 %for the datasets of Harvard University, BraTS −2018, −2019, −2020, −2021 and Kaggle respectively. Apart from this, promising sensitivity rate of the model helps us to avoid the risk of false negativity. Altogether, these qualities make the proposed model superior to the state of art techniques.